Struct ExprGraph 

pub struct ExprGraph {
    pub nodes: Vec<ExprNode>,
    pub edges: Vec<ExprEdge>,
    pub entry_return: Option<NodeId>,
    pub loss_nodes: Vec<NodeId>,
    pub param_nodes: Vec<NodeId>,
    pub tensor_nodes: Vec<NodeId>,
    pub dtype_conflicts: Vec<DtypeConflict>,
    pub dtype_risks: Vec<DtypeRisk>,
    pub numeric_domain_violations: Vec<NumericDomainViolation>,
    pub zero_times_infinity: Vec<ZeroTimesInfinity>,
    pub diagnostics: Vec<DataflowDiagnostic>,
}

Fields

nodes: Vec<ExprNode>

edges: Vec<ExprEdge>

entry_return: Option<NodeId>

Entrypoint return node, when analysis produced one.

loss_nodes: Vec<NodeId>

Nodes that are loss sinks (cross_entropy, mse, …).

param_nodes: Vec<NodeId>

Trainable / frozen parameter leaves discovered during analysis.

tensor_nodes: Vec<NodeId>

Nodes proven to be Candle tensor values. Non-tensor expressions stay out of ModelIr.

dtype_conflicts: Vec<DtypeConflict>

dtype_risks: Vec<DtypeRisk>

numeric_domain_violations: Vec<NumericDomainViolation>

zero_times_infinity: Vec<ZeroTimesInfinity>

diagnostics: Vec<DataflowDiagnostic>

Implementations

impl ExprGraph

pub fn node(&self, id: NodeId) -> &ExprNode

pub fn edge(&self, id: EdgeId) -> &ExprEdge

pub fn dead_params(&self) -> Vec<NodeId>

Trainable leaves with no reverse data path to any loss node.

pub fn apply_inference_mode(&mut self)

Sever autograd connectivity as in inference / no_grad mode.

pub fn severing_edges(&self) -> Vec<EdgeId>

Edges marked as severing autograd.

pub fn dtype_conflicts(&self) -> &[DtypeConflict]

Same-dtype operand mismatches recorded during transfer.

pub fn dtype_risks(&self) -> &[DtypeRisk]

Partially-known operands at an op that requires matching dtypes.

pub fn paths_to(&self, from: NodeId, to: NodeId) -> Vec<Vec<NodeId>>

Differentiable paths from from to to; severing edges are intentionally excluded. Caps path count to keep recursion honest on dense graphs.

pub fn reachable_from(&self, start: NodeId) -> HashSet<NodeId>

Forward-reachable nodes from start along non-severing edges.

pub fn classify_numeric_impacts(&mut self)

Classify numeric hazards by whether they can fail training or interfere with inference.

Trait Implementations

impl Clone for ExprGraph

fn clone(&self) -> ExprGraph

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ExprGraph

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ExprGraph

fn default() -> ExprGraph

Returns the “default value” for a type.

impl Serialize for ExprGraph

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.