concepts.dsl.executors.tensor_value_executor.TensorValueExecutorBase#

class TensorValueExecutorBase[source]#

Bases: DSLExecutorBase

The base class for tensor value executors.

Methods

`check_constraint`(constraint[, state])
`check_eq_constraint`(dtype, x, y, target[, state])
`execute`(expression[, state, bounded_variables])	Execute an expression.
`get_bounded_variable`(variable)	Get the value of a bounded variable.
`get_function_implementation`(name)	Get the implementation of a function.
`has_function_implementation`(name)	Check whether the executor has an implementation for a function.
`new_bounded_variables`(bvdict)	A context manager to add additional bounded variables to the executor.
`parse`(expression)	Parse an expression.
`register_function`(name, func)	Register an implementation for a function to the executor.
`register_function_implementation`(name, func)	Register an implementation for a function.
`retrieve_bounded_variable_by_name`(name)	Retrieve a bounded variable by its name.
`set_parser`(parser)	Set the parser for the executor.
`with_bounded_variables`(bvdict)	A context manager to set the bounded variables for the executor.
`with_state`([state])	A context manager to temporarily set the state of the executor.

Attributes

`bounded_variables`	The bounded variables for the execution.
`domain`	The domain of the executor.
`function_implementations`	The implementations of functions, which is a mapping from function names to implementations.
`parser`	The parser for the domain.
`pyobj_store`	The Python object store.
`state`	The current state of the environment.
`value_quantizer`	The value quantizer.

__init__(domain, parser=None)[source]#

Initialize the base class for tensor value executors.

Parameters:

domain (DSLDomainBase) – the domain of the executor.
parser (ParserBase | None) – the parser to use. If None, no parser will be used.

__new__(**kwargs)#

check_constraint(constraint, state=None)[source]#

Parameters:

constraint (Constraint)
state (TensorState | None)

check_eq_constraint(dtype, x, y, target, state=None)[source]#

Parameters:

dtype (TensorValueTypeBase)
x (TensorValue)
y (TensorValue)
target (bool)
state (TensorState | None)

Return type:

bool

execute(expression, state=None, bounded_variables=None)[source]#

Execute an expression.

Parameters:

expression (Expression | str) – the expression to execute.
state (TensorState | None) – the state to use. If None, the current state of the executor will be used.
bounded_variables (None | Sequence[Variable] | Dict[str | Variable, str | int | slice | bool | float | Tensor | TensorValue | ObjectConstant | StateObjectReference] | Dict[str, Dict[str, StateObjectReference | slice | TensorValue]]) – the bounded variables to use. If None, the current bounded variables of the executor will be used.

Returns:

the TensorValue object.

Return type:

get_bounded_variable(variable)[source]#

Get the value of a bounded variable.

Parameters:: variable (Variable) – the variable.
Returns:: the value of the variable.
Return type:: TensorValue | slice | StateObjectReference

get_function_implementation(name)#

Get the implementation of a function. When the executor does not have an implementation for the function, the implementation of the function in the domain will be returned. If that is also None, a KeyError will be raised.

Parameters:: name (str) – the name of the function.
Returns:: the implementation of the function.
Return type:: Callable

has_function_implementation(name)#

Check whether the executor has an implementation for a function.

Parameters:: name (str) – the name of the function.
Returns:: whether the executor has an implementation for the function.
Return type:: bool

new_bounded_variables(bvdict)[source]#

A context manager to add additional bounded variables to the executor.

Parameters:: bvdict (None | Sequence[Variable] | Dict[str | Variable, str | int | slice | bool | float | Tensor | TensorValue | ObjectConstant | StateObjectReference] | Dict[str, Dict[str, StateObjectReference | slice | TensorValue]]) – the new bounded variables.

parse(expression)[source]#

Parse an expression.

Parameters:: expression (Expression | str) – the expression to parse. When the input is already an expression, it will be returned directly.
Returns:: the parsed expression.

register_function(name, func)#

Parameters:

name (str) – the name of the function.
func (Callable) – the implementation of the function.

register_function_implementation(name, func)#

Parameters:

name (str) – the name of the function.
func (Callable) – the implementation of the function.

retrieve_bounded_variable_by_name(name)[source]#

Retrieve a bounded variable by its name.

Parameters:: name (str) – the name of the variable.
Returns:: the value of the variable.
Return type:: TensorValue | slice | StateObjectReference

set_parser(parser)[source]#

Set the parser for the executor.

Parameters:: parser (ParserBase) – the parser.

with_bounded_variables(bvdict)[source]#

A context manager to set the bounded variables for the executor.

Parameters:: bvdict (None | Sequence[Variable] | Dict[str | Variable, str | int | slice | bool | float | Tensor | TensorValue | ObjectConstant | StateObjectReference] | Dict[str, Dict[str, StateObjectReference | slice | TensorValue]]) – the bounded variables.

with_state(state=None)[source]#

A context manager to temporarily set the state of the executor.

Parameters:: state (TensorState | None)

property bounded_variables: Dict[str, Dict[str, StateObjectReference | slice | TensorValue]]#: The bounded variables for the execution. Note that most of the time you should use the get_bounded_variable() method to get values for the bounded variable.

property domain: DSLDomainBase#: The domain of the executor.

property function_implementations: Dict[str, Callable]#: The implementations of functions, which is a mapping from function names to implementations.

property parser: ParserBase | None#: The parser for the domain.

property pyobj_store: PyObjectStore#: The Python object store.

property state: TensorState | None#: The current state of the environment.

property value_quantizer: ValueQuantizer#: The value quantizer.