flodl 0.5.2

floDl — a flow-graph deep learning framework built on libtorch
Documentation 
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//! Verbose build-time output for graph tree inspection.

use std::fmt::Write;
use crate::nn::Module;
use super::Graph;

impl Graph {
    /// Generate a tree summary showing subgraph structure, tags, and parameter counts.
    pub fn tree_summary(&self) -> String {
        let mut out = String::new();
        let _ = writeln!(out, "=== Graph Tree ===");
        self.write_tree_node(&mut out, "", true);
        let _ = writeln!(out);
        let _ = writeln!(out, "{}", self.param_summary());
        out
    }

    fn write_tree_node(&self, out: &mut String, indent: &str, is_root: bool) {
        let name = self.label().unwrap_or("(root)");
        let hash = &self.structural_hash()[..8];
        let param_count: usize = self.parameters().len();
        let frozen_count: usize = self.parameters().iter().filter(|p| p.is_frozen()).count();

        if is_root {
            let _ = writeln!(out, "{} [hash: {}]", name, hash);
        }

        // List tags (non-internal only)
        let mut tags: Vec<&str> = self.tag_names.keys()
            .filter(|t| !self.internal_tags.contains(*t))
            .map(|s| s.as_str())
            .collect();
        tags.sort();

        if !tags.is_empty() {
            let _ = writeln!(out, "{}+-- tags: {}", indent, tags.join(", "));
        }

        // Show param count with frozen info
        if frozen_count > 0 && frozen_count == param_count {
            let _ = writeln!(out, "{}+-- params: {} [frozen]", indent, param_count);
        } else if frozen_count > 0 {
            let _ = writeln!(out, "{}+-- params: {} ({} frozen)", indent, param_count, frozen_count);
        } else if param_count > 0 {
            let _ = writeln!(out, "{}+-- params: {}", indent, param_count);
        }

        // Recurse into children
        let mut child_entries: Vec<(String, usize)> = self.children.iter()
            .map(|(label, &ni)| (label.clone(), ni))
            .collect();
        child_entries.sort_by_key(|(_, ni)| *ni);

        for (i, (label, ni)) in child_entries.iter().enumerate() {
            let is_last = i == child_entries.len() - 1;
            let child_indent = if is_last {
                format!("{}    ", indent)
            } else {
                format!("{}|   ", indent)
            };

            if let Some(ref module) = self.nodes[*ni].module {
                if let Some(child_graph) = module.as_graph() {
                    let child_hash = &child_graph.structural_hash()[..8];
                    let child_params = child_graph.parameters().len();
                    let child_frozen = child_graph.parameters().iter()
                        .filter(|p| p.is_frozen()).count();

                    let frozen_str = if child_frozen > 0 && child_frozen == child_params {
                        " * frozen"
                    } else {
                        ""
                    };
                    let _ = writeln!(out, "{}+-- {} [hash: {}]{}", indent, label, child_hash, frozen_str);
                    child_graph.write_tree_node(out, &child_indent, false);
                }
            }
        }
    }

    /// Per-subgraph parameter count breakdown.
    pub fn param_summary(&self) -> String {
        let mut out = String::new();
        let total: usize = self.parameters().len();
        let _ = writeln!(out, "=== Parameter Summary ===");
        let _ = writeln!(out, "Total: {} parameters", total);

        if !self.children.is_empty() {
            let mut child_entries: Vec<(String, usize)> = self.children.iter()
                .map(|(label, &ni)| (label.clone(), ni))
                .collect();
            child_entries.sort_by_key(|(_, ni)| *ni);

            let mut accounted = 0usize;
            for (label, ni) in &child_entries {
                if let Some(ref module) = self.nodes[*ni].module {
                    if let Some(child_graph) = module.as_graph() {
                        let count = child_graph.parameters().len();
                        let frozen = child_graph.parameters().iter()
                            .filter(|p| p.is_frozen()).count();
                        let pct = if total > 0 { count as f64 / total as f64 * 100.0 } else { 0.0 };
                        let frozen_str = if frozen == count && count > 0 {
                            "  frozen".to_string()
                        } else if frozen > 0 {
                            format!("  {}/{} frozen", frozen, count)
                        } else {
                            "  trainable".to_string()
                        };
                        let _ = writeln!(out, "  {}: {} ({:.1}%){}", label, count, pct, frozen_str);
                        accounted += count;
                    }
                }
            }
            let own = total.saturating_sub(accounted);
            if own > 0 {
                let pct = if total > 0 { own as f64 / total as f64 * 100.0 } else { 0.0 };
                let _ = writeln!(out, "  (own): {} ({:.1}%)  trainable", own, pct);
            }
        }
        out
    }
}