Random

Random types

DistributionType

A random distribution type

Syntax:

!random.distribution<
  ::mlir::heir::random::Distribution   # distribution_type
>

A generic type, representing a specific random distribution type of either uniform or gaussian as an attribute ($distribution_type).

Parameters:

PRNGType

A pseudorandom number generator type

Syntax: !random.prng

A type that provides pseudorandom number generator.

Random ops

random.discrete_gaussian_distribution (heir::random::DiscreteGaussianDistributionOp)

Initializes the Discrete Gaussian Distribution

Syntax:

operation ::= `random.discrete_gaussian_distribution` operands attr-dict `:` functional-type(operands, results)

Initializes the Discrete Gaussian Distribution. The distribution is initialized with a mean and a standard deviation and pseudorandom generator that provides the source of the randomness.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

random.discrete_uniform_distribution (heir::random::DiscreteUniformDistributionOp)

Initializes the Discrete Uniform Distribution

Syntax:

operation ::= `random.discrete_uniform_distribution` $input `{` `range` `=` `[` $min `,` $max `]` `}` attr-dict `:` `(` qualified(type($input)) `)` `->` type($output)

Initializes the Discrete Uniform Distribution. The distribution is initialized with a minimum and a maximum value and pseudo random generator that provides the source of the randomness. The distribution is inclusive of the minimum and exclusive of the maximum.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

random.init_prng (heir::random::InitOp)

Initializes the pseudorandom number generator with a seed.

Syntax:

operation ::= `random.init_prng` operands attr-dict `:` functional-type(operands, results)

Initializes the PRNG with a seed. The seed is dynamically provided due to protocols that agree on shared randomness. The PRNG is used to initialized the random distributions such as the discrete gaussian distribution and the discrete uniform distribution. This initialization also takes as input a number of bits that are generated for each number value sampled (num_bits). For instance, a num_bits of 32 will mean that distributions will generate a 32-bit integer value. We expect that the seed initialization is done statically and globally once per thread for all distributions; however, if multiple threads are generating randomness, then seed initialization should be done per thread; otherwise there is no guarantee of consistent behavior. Thread safety is so far not considered.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

random.sample (heir::random::SampleOp)

Samples from a distribution

Syntax:

operation ::= `random.sample` operands attr-dict `:` functional-type(operands, results)

Samples from the distribution to obtain a random value or tensor of values.

Operands:

Results: