tensorlogic-infer 0.1.0

//! Simplified distributed execution demonstration.
//!
//! This example shows the basic structure for distributed execution concepts.
//! Full distributed execution features are planned for future releases.
//!
//! Run with: cargo run --example distributed_demo

use tensorlogic_infer::{DummyExecutor, DummyTensor};
use tensorlogic_ir::{EinsumGraph, EinsumNode, OpType};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("=== TensorLogic Distributed Execution Demo ===\n");
    println!("NOTE: This is a simplified demonstration.");
    println!("Full distributed features are under development.\n");

    // Create a sample computation graph
    let graph = create_sample_graph();
    println!(
        "Created computation graph with {} nodes, {} tensors\n",
        graph.nodes.len(),
        graph.tensors.len()
    );

    // Validate the graph
    graph.validate()?;
    println!("✓ Graph validation successful\n");

    // Demonstrate executor setup
    println!("--- Executor Setup ---");
    demo_executor_setup(&graph)?;

    println!("\n=== Demo Complete ===");
    Ok(())
}

fn create_sample_graph() -> EinsumGraph {
    let mut graph = EinsumGraph::new();

    // Create tensors
    let x = graph.add_tensor("x");
    let weights = graph.add_tensor("weights");
    let matmul_out = graph.add_tensor("matmul_out");
    let final_out = graph.add_tensor("final_out");

    // Einsum operation (matrix multiplication)
    let einsum_node = EinsumNode {
        op: OpType::Einsum {
            spec: "ij,jk->ik".to_string(),
        },
        inputs: vec![x, weights],
        outputs: vec![matmul_out],
        metadata: None,
    };
    graph.nodes.push(einsum_node);

    // Activation (ReLU)
    let relu_node = EinsumNode {
        op: OpType::ElemUnary {
            op: "relu".to_string(),
        },
        inputs: vec![matmul_out],
        outputs: vec![final_out],
        metadata: None,
    };
    graph.nodes.push(relu_node);

    // Mark final output
    graph.add_output(final_out).ok();

    graph
}

fn demo_executor_setup(_graph: &EinsumGraph) -> Result<(), Box<dyn std::error::Error>> {
    println!("Setting up DummyExecutor...");

    // Create executor
    let _executor = DummyExecutor::new();
    println!("  ✓ Executor created");

    // Create sample tensors
    let _x = DummyTensor::new("x", vec![64, 128]);
    let _weights = DummyTensor::new("weights", vec![128, 256]);
    println!("  ✓ Sample tensors created");

    println!("\nNote: Graph execution requires implementing the TlExecutor trait methods.");
    println!("For distributed execution, additional coordinator and communication");
    println!("backend implementations will be needed.");

    Ok(())
}