Module export 

Export compiled TensorLogic graphs to various formats.

This module provides functionality to export EinsumGraph instances to different interchange formats for execution on various backends.

Supported Formats

• ONNX (onnx feature): Export to ONNX format for use with ONNX Runtime, PyTorch, TensorFlow, and other ONNX-compatible frameworks.
• TensorFlow GraphDef (tensorflow feature): Export to TensorFlow GraphDef format for execution within TensorFlow runtime and SavedModel workflows.
• PyTorch Code (pytorch feature): Generate PyTorch nn.Module Python code for integration with PyTorch workflows and TorchScript compilation.

Example

use tensorlogic_compiler::export::onnx::export_to_onnx;
use tensorlogic_compiler::compile_to_einsum;
use tensorlogic_ir::{TLExpr, Term};

let expr = TLExpr::pred("knows", vec![Term::var("x"), Term::var("y")]);
let graph = compile_to_einsum(&expr)?;

// Export to ONNX
let onnx_bytes = export_to_onnx(&graph, "logic_model")?;
std::fs::write("model.onnx", onnx_bytes)?;

// Export to TensorFlow
#[cfg(feature = "tensorflow")]
{
    use tensorlogic_compiler::export::tensorflow::export_to_tensorflow;
    let tf_bytes = export_to_tensorflow(&graph, "logic_model")?;
    std::fs::write("model.pb", tf_bytes)?;
}

// Export to PyTorch
#[cfg(feature = "pytorch")]
{
    use tensorlogic_compiler::export::pytorch::export_to_pytorch;
    let pytorch_code = export_to_pytorch(&graph, "LogicModel")?;
    std::fs::write("model.py", pytorch_code)?;
}