Struct CompiledProgram 

pub struct CompiledProgram { /* private fields */ }

Implementations

impl CompiledProgram

pub fn evaluate( &self, _py: Python<'_>, return_grads: bool, samples: Option<usize>, seed: Option<u64>, confidence: f64, max_nonmonotone_iterations: usize, sampling_method: Option<String>, memory_mb: Option<u64>, allow_cpu_oracle: bool, ) -> PyResult<EvalResult>

pub fn prob_var_map(&self, py: Python<'_>) -> PyResult<Vec<Py<PyAny>>>

Which probabilistic fact each CNF variable stands for.

The returned list’s length is the CNF encoder’s variable capacity, not the number of CNF variables in use and not the number of random variables in the program — a real fraction of entries are {"kind": "other"} padding (do not use len() of the result as a variable count). Entry i describes CNF variable i — the same position i that the grad_true / grad_false vectors of evaluate(return_grads=True) use (index 0 is unused padding, since CNF variables are 1-indexed).

For a "choice" entry (one Bernoulli decision of an annotated disjunction’s chain), probs[choice_index] is the disjunction’s declared, marginal probability and is display context only; prob is the conditional Bernoulli parameter actually assigned to this variable’s weight, and prob * (1 - prob) — not probs[choice_index] * (1 - probs[choice_index]) — is the correct Jacobian for grad_true / grad_false at this position.

Raises ValueError for Monte Carlo programs, and for exact programs compiled through the GPU count-lift fast path (count aggregates without evidence or annotated disjunctions), since that path never builds a CNF encoding and therefore has no variable map to report — this is not the same as the program having no probabilistic facts.

pub fn evaluate_device( &self, py: Python<'_>, samples: Option<usize>, seed: Option<u64>, confidence: f64, max_nonmonotone_iterations: usize, sampling_method: Option<String>, memory_mb: Option<u64>, ) -> PyResult<McDeviceEvalResult>

Evaluate Monte Carlo programs and return device-only result counts via DLPack.

This is the primary GPU-native API surface for MC inference. It never performs device->host reads for result data (only returns device buffers).

pub fn zero_grad(&self, py: Python<'_>) -> PyResult<()>

Zero gradients for all registered networks.

This should be called at the start of each training iteration to clear accumulated gradients from previous iterations.

pub fn optimizer_step(&self, py: Python<'_>) -> PyResult<()>

Perform optimizer step for all registered networks.

This applies the accumulated gradients to update network parameters. Should be called after forward_backward().

pub fn clip_grad_norms(&self, py: Python<'_>, max_norm: f64) -> PyResult<()>

Clip gradient norms for all registered networks.

Uses torch.nn.utils.clip_grad_norm_.

pub fn evaluate_loss(&self, queries: Vec<String>) -> PyResult<f64>

Evaluate mean NLL loss over queries without updating parameters.

Useful for validation/test set evaluation.

Arguments

• queries - List of query strings to evaluate

Returns

Mean NLL loss over all queries

pub fn memory_stats(&self, py: Python<'_>) -> PyResult<Py<PyAny>>

Return memory diagnostics including allocated_bytes and memory_limit_bytes.

pub fn rule_provenance(&self, py: Python<'_>) -> PyResult<Py<PyAny>>

pub fn proof_traces(&self, py: Python<'_>) -> PyResult<Py<PyAny>>

pub fn host_transfer_stats(&self, py: Python<'_>) -> PyResult<Py<PyAny>>

pub fn reset_host_transfer_stats(&self)

pub fn neural_hot_loop_diagnostics(&self, py: Python<'_>) -> PyResult<Py<PyAny>>

pub fn cuda_graph_stats(&self, py: Python<'_>) -> PyResult<Py<PyAny>>

Trait Implementations

impl DerefToPyAny for CompiledProgram

impl ExtractPyClassWithClone for CompiledProgram

impl<'py> IntoPyObject<'py> for CompiledProgram

type Target = CompiledProgram

The Python output type

type Output = Bound<'py, <CompiledProgram as IntoPyObject<'py>>::Target>

The smart pointer type to use.

type Error = PyErr

The type returned in the event of a conversion error.

fn into_pyobject( self, py: Python<'py>, ) -> Result<<Self as IntoPyObject<'_>>::Output, <Self as IntoPyObject<'_>>::Error>

Performs the conversion.

impl PyClass for CompiledProgram

const NAME: &str = "CompiledProgram"

Name of the class.

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for CompiledProgram

const MODULE: Option<&str> = ::core::option::Option::None

Module which the class will be associated with.

const IS_BASETYPE: bool = false

#[pyclass(subclass)]

const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]

const IS_MAPPING: bool = false

#[pyclass(mapping)]

const IS_SEQUENCE: bool = false

#[pyclass(sequence)]

const IS_IMMUTABLE_TYPE: bool = false

#[pyclass(immutable_type)]

const RAW_DOC: &'static CStr = c"\x00"

Docstring for the class provided on the struct or enum.

const DOC: &'static CStr

Fully rendered class doc, including the text_signature if a constructor is defined.

type Layout = <<CompiledProgram as PyClassImpl>::BaseNativeType as PyClassBaseType>::Layout<CompiledProgram>

Description of how this class is laid out in memory

type BaseType = PyAny

Base class

type ThreadChecker = NoopThreadChecker

This handles following two situations:

type Inventory = Pyo3MethodsInventoryForCompiledProgram

type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable

type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.

type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.

type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.

fn items_iter() -> PyClassItemsIter

fn lazy_type_object() -> &'static LazyTypeObject<Self>

fn dict_offset() -> Option<PyObjectOffset>

Used to provide the dictoffset slot (equivalent to tp_dictoffset)

fn weaklist_offset() -> Option<PyObjectOffset>

Used to provide the weaklistoffset slot (equivalent to tp_weaklistoffset

impl PyTypeInfo for CompiledProgram

const NAME: &str = <Self as ::pyo3::PyClass>::NAME

👎Deprecated since 0.28.0:

prefer using ::type_object(py).name() to get the correct runtime value

Class name.

const MODULE: Option<&str> = <Self as ::pyo3::impl_::pyclass::PyClassImpl>::MODULE

👎Deprecated since 0.28.0:

prefer using ::type_object(py).module() to get the correct runtime value

Module name, if any.

fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.

fn type_object(py: Python<'_>) -> Bound<'_, PyType>

Returns the safe abstraction over the type object.

fn is_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type or a subclass of this type.

fn is_exact_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type.