class ov::op::v13::Multinomial¶
Overview¶
Multinomial operation creates a sequence of indices of classes sampled from the multinomial distribution. More…
#include <multinomial.hpp>
class Multinomial: public ov::op::Op
{
public:
// construction
Multinomial();
Multinomial(
const Output<Node>& input,
const Output<Node>& num_samples,
const ov::element::Type_t output_type,
const bool with_replacement,
const bool log_probs,
const uint64_t global_seed = 0,
const uint64_t op_seed = 0
);
// methods
OPENVINO_OP("Multinomial", "opset13");
virtual bool visit_attributes(AttributeVisitor& visitor);
virtual void validate_and_infer_types();
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const;
ov::element::Type_t get_convert_type() const;
bool get_with_replacement() const;
bool get_log_probs() const;
uint64_t get_global_seed() const;
uint64_t get_op_seed() const;
void set_convert_type(const ov::element::Type_t output_type);
void set_with_replacement(const bool with_replacement);
void set_log_probs(const bool log_probs);
void set_global_seed(const uint64_t global_seed);
void set_op_seed(const uint64_t op_seed);
};Inherited Members¶
public:
// typedefs
typedef DiscreteTypeInfo type_info_t;
typedef std::map<std::string, Any> RTMap;
// methods
virtual void validate_and_infer_types();
void constructor_validate_and_infer_types();
virtual bool visit_attributes(AttributeVisitor&);
virtual const ov::op::AutoBroadcastSpec& get_autob() const;
virtual bool has_evaluate() const;
virtual bool evaluate(
const ov::HostTensorVector& output_values,
const ov::HostTensorVector& input_values
) const;
virtual bool evaluate(
const ov::HostTensorVector& output_values,
const ov::HostTensorVector& input_values,
const EvaluationContext& evaluationContext
) const;
virtual bool evaluate(
ov::TensorVector& output_values,
const ov::TensorVector& input_values
) const;
virtual bool evaluate(
ov::TensorVector& output_values,
const ov::TensorVector& input_values,
const ov::EvaluationContext& evaluationContext
) const;
virtual bool evaluate_lower(ov::TensorVector& output_values) const;
virtual bool evaluate_upper(ov::TensorVector& output_values) const;
virtual bool evaluate_label(TensorLabelVector& output_labels) const;
virtual bool constant_fold(
OutputVector& output_values,
const OutputVector& inputs_values
);
virtual OutputVector decompose_op() const;
virtual const type_info_t& get_type_info() const = 0;
const char \* get_type_name() const;
void set_arguments(const NodeVector& arguments);
void set_arguments(const OutputVector& arguments);
void set_argument(size_t position, const Output<Node>& argument);
void set_output_type(
size_t i,
const element::Type& element_type,
const PartialShape& pshape
);
void set_output_size(size_t output_size);
void invalidate_values();
virtual void revalidate_and_infer_types();
virtual std::string description() const;
const std::string& get_name() const;
void set_friendly_name(const std::string& name);
const std::string& get_friendly_name() const;
virtual bool is_dynamic() const;
size_t get_instance_id() const;
virtual std::ostream& write_description(std::ostream& os, uint32_t depth = 0) const;
const std::vector<std::shared_ptr<Node>>& get_control_dependencies() const;
const std::vector<Node \*>& get_control_dependents() const;
void add_control_dependency(std::shared_ptr<Node> node);
void remove_control_dependency(std::shared_ptr<Node> node);
void clear_control_dependencies();
void clear_control_dependents();
void add_node_control_dependencies(const std::shared_ptr<const Node>& source_node);
void add_node_control_dependents(const std::shared_ptr<const Node>& source_node);
void transfer_control_dependents(std::shared_ptr<Node> replacement);
size_t get_output_size() const;
const element::Type& get_output_element_type(size_t i) const;
const element::Type& get_element_type() const;
const Shape& get_output_shape(size_t i) const;
const PartialShape& get_output_partial_shape(size_t i) const;
Output<const Node> get_default_output() const;
Output<Node> get_default_output();
virtual size_t get_default_output_index() const;
size_t no_default_index() const;
const Shape& get_shape() const;
descriptor::Tensor& get_output_tensor(size_t i) const;
descriptor::Tensor& get_input_tensor(size_t i) const;
std::set<Input<Node>> get_output_target_inputs(size_t i) const;
size_t get_input_size() const;
const element::Type& get_input_element_type(size_t i) const;
const Shape& get_input_shape(size_t i) const;
const PartialShape& get_input_partial_shape(size_t i) const;
Node \* get_input_node_ptr(size_t index) const;
std::shared_ptr<Node> get_input_node_shared_ptr(size_t index) const;
Output<Node> get_input_source_output(size_t i) const;
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const = 0;
std::shared_ptr<Node> copy_with_new_inputs(const OutputVector& new_args) const;
std::shared_ptr<Node> copy_with_new_inputs(
const OutputVector& inputs,
const std::vector<std::shared_ptr<Node>>& control_dependencies
) const;
bool has_same_type(std::shared_ptr<const Node> node) const;
RTMap& get_rt_info();
const RTMap& get_rt_info() const;
NodeVector get_users(bool check_is_used = false) const;
bool operator < (const Node& other) const;
std::vector<Input<Node>> inputs();
std::vector<Input<const Node>> inputs() const;
std::vector<Output<Node>> input_values() const;
std::vector<Output<Node>> outputs();
std::vector<Output<const Node>> outputs() const;
Input<Node> input(size_t input_index);
Input<const Node> input(size_t input_index) const;
Output<Node> input_value(size_t input_index) const;
Output<Node> output(size_t output_index);
Output<const Node> output(size_t output_index) const;
virtual bool match_value(
ov::pass::pattern::Matcher \* matcher,
const Output<Node>& pattern_value,
const Output<Node>& graph_value
);
virtual bool match_node(
ov::pass::pattern::Matcher \* matcher,
const Output<Node>& graph_value
);
static _OPENVINO_HIDDEN_METHODconst ::ov::Node::type_info_t& get_type_info_static();
virtual const ::ov::Node::type_info_t& get_type_info() const;Detailed Documentation¶
Multinomial operation creates a sequence of indices of classes sampled from the multinomial distribution.
Construction¶
Multinomial(
const Output<Node>& input,
const Output<Node>& num_samples,
const ov::element::Type_t output_type,
const bool with_replacement,
const bool log_probs,
const uint64_t global_seed = 0,
const uint64_t op_seed = 0
)Multinomial operation creates a sequence of indices of classes sampled from the multinomial distribution.
Parameters:
probs |
Input tensor containing at each index poisition probability/log probability of sampling a given class. Any floating-point precision values are allowed. |
num_samples |
Scalar or 1D tensor with a single value that determines the number of samples to generate per batch. Values should be of an integer type. |
convert_type |
Data type to which to convert the output class indices. Allowed values: i32/i64 |
with_replacement |
Boolean that determines whether a sampled class can appear more than once in the output. |
log_probs |
Boolean that determines whether to treat input probabilities as log probabilities. |
global_seed |
First seed value (key) of Phillox random number generation algorithm. (See RandomUniform for details) |
op_seed |
Second seed value (counter) of Phillox random number generation algorithm. (See RandomUniform for details) |
Methods¶
virtual void validate_and_infer_types()Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.