core/deployment.py

"""Deployment utilities for SciMLx models (ONNX, TensorRT, Quantization)."""

import torch
import torch.nn as nn
from pathlib import Path

def export_to_onnx(model: nn.Module, input_size: tuple, export_path: str, opset_version: int = 17):
    """
    Export a SciML model to ONNX format.
    Includes physical sanity checks metadata if available.
    """
    model.eval()
    dummy_input = torch.randn(input_size)
    
    # Ensure export path is a Path object
    path = Path(export_path)
    path.parent.mkdir(parents=True, exist_ok=True)
    
    print(f"[Deployment] Exporting model to {export_path}...")
    torch.onnx.export(
        model,
        dummy_input,
        export_path,
        export_params=True,
        opset_version=opset_version,
        do_constant_folding=True,
        input_names=['input'],
        output_names=['output'],
        dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}}
    )
    print(f"[Deployment] ✓ Export complete.")

def apply_static_quantization(model: nn.Module):
    """
    Apply basic static quantization (INT8) to the model.
    Placeholder for physics-preserving quantization logic.
    """
    # This is a placeholder for PyTorch's quantization API
    print("[Deployment] Applying static quantization (PTQ)...")
    quantized_model = torch.quantization.quantize_dynamic(
        model, {nn.Linear}, dtype=torch.qint8
    )
    return quantized_model

class DifferentiableDigitalTwin:
    """
    Wrapper for a deployed SciML model that includes runtime sanity checks.
    """
    def __init__(self, onnx_path: str):
        self.onnx_path = onnx_path
        # In a real implementation, we would use onnxruntime here
        
    def verify_conservation(self, input_data, output_data):
        """
        Runtime check for conservation of mass/energy.
        """
        # Placeholder for actual conservation law check
        return True