Backend Integration

class Backend:
    """
    Abstract base class for ONNX execution backends.
    
    Provides interface for preparing and running ONNX models
    on various compute devices and runtimes.
    """
    
    @classmethod
    def is_compatible(cls, model, device="CPU", **kwargs):
        """
        Check if backend can execute the given model.

        Parameters:
        - model: ModelProto to check compatibility for
        - device: Target device ("CPU", "CUDA", etc.)
        - **kwargs: Additional backend-specific options

        Returns:
        bool: True if backend can execute the model, False otherwise
        """
    
    @classmethod  
    def prepare(cls, model, device="CPU", **kwargs):
        """
        Prepare model for execution on this backend.

        Parameters:
        - model: ModelProto to prepare
        - device: Target device for execution
        - **kwargs: Backend-specific preparation options

        Returns:
        BackendRep: Prepared model representation ready for execution

        Raises:
        BackendIsNotSupposedToImplementIt: If backend doesn't support preparation
        """
    
    @classmethod
    def run_model(cls, model, inputs, device="CPU", **kwargs):
        """
        Run model once with given inputs.

        Parameters:
        - model: ModelProto to execute
        - inputs: Input data as list of numpy arrays
        - device: Target device for execution
        - **kwargs: Execution options

        Returns:
        namedtuple: Output values with names corresponding to model outputs

        Raises:
        BackendIsNotSupposedToImplementIt: If backend doesn't support direct execution
        """
    
    @classmethod
    def run_node(cls, node, inputs, device="CPU", outputs_info=None, **kwargs):
        """
        Run a single node with given inputs.

        Parameters:
        - node: NodeProto to execute
        - inputs: Input data as list of numpy arrays
        - device: Target device for execution
        - outputs_info: Optional output type information
        - **kwargs: Execution options

        Returns:
        namedtuple: Output values

        Raises:
        BackendIsNotSupposedToImplementIt: If backend doesn't support node execution
        """
    
    @classmethod
    def supports_device(cls, device):
        """
        Check if backend supports the specified device.

        Parameters:
        - device: Device identifier to check

        Returns:
        bool: True if device is supported, False otherwise
        """

class BackendRep:
    """
    Backend representation of a prepared model.
    
    Contains the model in a backend-specific format
    optimized for repeated execution.
    """
    
    def run(self, inputs, **kwargs):
        """
        Execute the prepared model with given inputs.

        Parameters:
        - inputs: Input data as list of numpy arrays
        - **kwargs: Execution options

        Returns:
        namedtuple: Output values with names corresponding to model outputs
        """

class Device:
    """
    Device specification for backend execution.
    
    Encapsulates device type and device-specific configuration.
    """
    
    def __init__(self, device_type, device_id=0):
        """
        Initialize device specification.

        Parameters:
        - device_type: Type of device (CPU, CUDA, etc.)
        - device_id: Device identifier for multi-device systems
        """

class DeviceType:
    """
    Constants for standard device types.
    """
    CPU = "CPU"
    CUDA = "CUDA" 
    # Additional device types may be defined by specific backends

def namedtupledict(typename, field_names, *args, **kwargs):
    """
    Create a named tuple class with dictionary-like access.

    Parameters:
    - typename: Name for the named tuple class
    - field_names: Field names for the tuple
    - *args, **kwargs: Additional arguments for namedtuple creation

    Returns:
    type: Named tuple class with dict-like access methods
    """

import onnx
from onnx import backend
import numpy as np
from collections import namedtuple

class MyCustomBackend(backend.Backend):
    """Example implementation of a custom ONNX backend."""
    
    @classmethod
    def is_compatible(cls, model, device="CPU", **kwargs):
        """Check if we can run this model."""
        
        # Only support CPU for this example
        if device != "CPU":
            return False
            
        # Check if all operators are supported
        supported_ops = {"Add", "Sub", "Mul", "Div", "Relu", "MatMul", "Conv"}
        
        for node in model.graph.node:
            if node.op_type not in supported_ops:
                print(f"Unsupported operator: {node.op_type}")
                return False
                
        return True
    
    @classmethod
    def prepare(cls, model, device="CPU", **kwargs):
        """Prepare model for execution."""
        
        if not cls.is_compatible(model, device, **kwargs):
            raise RuntimeError("Model is not compatible with this backend")
            
        # Create a backend representation
        return MyBackendRep(model, device)
    
    @classmethod
    def run_model(cls, model, inputs, device="CPU", **kwargs):
        """Run model directly (without preparation)."""
        
        # Prepare and run
        rep = cls.prepare(model, device, **kwargs)
        return rep.run(inputs, **kwargs)
    
    @classmethod
    def run_node(cls, node, inputs, device="CPU", **kwargs):
        """Run a single node."""
        
        # Simple node execution logic
        if node.op_type == "Add":
            result = inputs[0] + inputs[1]
        elif node.op_type == "Mul":
            result = inputs[0] * inputs[1]
        elif node.op_type == "Relu":
            result = np.maximum(0, inputs[0])
        else:
            raise NotImplementedError(f"Node {node.op_type} not implemented")
            
        # Return as named tuple
        OutputTuple = namedtuple('Output', [f'output_{i}' for i in range(len(node.output))])
        return OutputTuple(result)
    
    @classmethod
    def supports_device(cls, device):
        """Check device support."""
        return device == "CPU"

class MyBackendRep(backend.BackendRep):
    """Backend representation for prepared models."""
    
    def __init__(self, model, device):
        self.model = model
        self.device = device
        self._prepare_model()
    
    def _prepare_model(self):
        """Internal preparation logic."""
        print(f"Preparing model '{self.model.graph.name}' for {self.device}")
        # In a real backend, this would compile/optimize the model
        
    def run(self, inputs, **kwargs):
        """Execute the prepared model."""
        
        print(f"Executing model with {len(inputs)} inputs")
        
        # Simple execution simulation
        # In a real backend, this would use optimized execution
        current_values = {}
        
        # Set input values
        for i, input_info in enumerate(self.model.graph.input):
            current_values[input_info.name] = inputs[i]
            
        # Set initializer values
        for initializer in self.model.graph.initializer:
            from onnx import numpy_helper
            current_values[initializer.name] = numpy_helper.to_array(initializer)
        
        # Execute nodes in order
        for node in self.model.graph.node:
            node_inputs = [current_values[name] for name in node.input]
            node_result = MyCustomBackend.run_node(node, node_inputs)
            
            # Store outputs
            for i, output_name in enumerate(node.output):
                if hasattr(node_result, f'output_{i}'):
                    current_values[output_name] = getattr(node_result, f'output_{i}')
                else:
                    current_values[output_name] = node_result[i] if isinstance(node_result, (list, tuple)) else node_result
        
        # Collect final outputs
        outputs = []
        output_names = []
        for output_info in self.model.graph.output:
            outputs.append(current_values[output_info.name])
            output_names.append(output_info.name)
        
        # Return as named tuple
        OutputTuple = namedtuple('ModelOutput', output_names)
        return OutputTuple(*outputs)

# Test the custom backend
def test_custom_backend():
    """Test the custom backend implementation."""
    
    # Create a simple model for testing
    from onnx import helper, TensorProto
    
    X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [2, 2])
    Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [2, 2])
    
    # Create a simple Add node
    add_node = helper.make_node('Add', ['X', 'X'], ['Y'])
    
    graph = helper.make_graph([add_node], 'test_model', [X], [Y])
    model = helper.make_model(graph)
    
    # Test backend compatibility
    backend_impl = MyCustomBackend()
    
    if backend_impl.is_compatible(model):
        print("✓ Model is compatible with custom backend")
        
        # Test direct execution
        test_input = np.array([[1, 2], [3, 4]], dtype=np.float32)
        result = backend_impl.run_model(model, [test_input])
        print(f"Direct execution result: {result.Y}")
        
        # Test prepared execution
        rep = backend_impl.prepare(model)
        result2 = rep.run([test_input])
        print(f"Prepared execution result: {result2.Y}")
        
        print(f"Results match: {np.array_equal(result.Y, result2.Y)}")
        
    else:
        print("✗ Model is not compatible with custom backend")

# Run the test
test_custom_backend()

import onnx
from onnx import backend
import numpy as np

class BackendTester:
    """Framework for testing ONNX backend implementations."""
    
    def __init__(self, backend_class):
        self.backend = backend_class
        
    def test_operator_support(self, test_cases):
        """Test backend support for various operators."""
        
        results = {}
        
        for op_name, test_model in test_cases.items():
            try:
                is_compatible = self.backend.is_compatible(test_model)
                results[op_name] = {
                    'compatible': is_compatible,
                    'error': None
                }
                
                if is_compatible:
                    # Try to prepare the model
                    rep = self.backend.prepare(test_model)
                    results[op_name]['preparable'] = True
                else:
                    results[op_name]['preparable'] = False
                    
            except Exception as e:
                results[op_name] = {
                    'compatible': False,
                    'preparable': False,
                    'error': str(e)
                }
        
        return results
    
    def test_device_support(self, devices):
        """Test backend device support."""
        
        device_results = {}
        for device in devices:
            device_results[device] = self.backend.supports_device(device)
            
        return device_results
    
    def benchmark_execution(self, model, inputs, iterations=100):
        """Benchmark model execution performance."""
        
        import time
        
        # Test direct execution
        start_time = time.time()
        for _ in range(iterations):
            result = self.backend.run_model(model, inputs)
        direct_time = time.time() - start_time
        
        # Test prepared execution  
        rep = self.backend.prepare(model)
        start_time = time.time()
        for _ in range(iterations):
            result = rep.run(inputs)
        prepared_time = time.time() - start_time
        
        return {
            'direct_execution_time': direct_time,
            'prepared_execution_time': prepared_time,
            'speedup': direct_time / prepared_time if prepared_time > 0 else float('inf'),
            'iterations': iterations
        }

# Example usage with custom backend
def test_backend_comprehensive():
    """Comprehensive backend testing example."""
    
    # Create test models for different operators
    from onnx import helper, TensorProto
    
    test_cases = {}
    
    # Add test
    X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [2, 2])
    Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [2, 2])
    add_node = helper.make_node('Add', ['X', 'X'], ['Y'])
    add_graph = helper.make_graph([add_node], 'add_test', [X], [Y])
    test_cases['Add'] = helper.make_model(add_graph)
    
    # ReLU test
    relu_node = helper.make_node('Relu', ['X'], ['Y']) 
    relu_graph = helper.make_graph([relu_node], 'relu_test', [X], [Y])
    test_cases['Relu'] = helper.make_model(relu_graph)
    
    # Unsupported operator test
    unsupported_node = helper.make_node('LSTM', ['X'], ['Y'])
    unsupported_graph = helper.make_graph([unsupported_node], 'unsupported_test', [X], [Y])
    test_cases['LSTM'] = helper.make_model(unsupported_graph)
    
    # Run tests
    tester = BackendTester(MyCustomBackend)
    
    print("=== Operator Support Test ===")
    op_results = tester.test_operator_support(test_cases)
    for op, result in op_results.items():
        status = "✓" if result['compatible'] else "✗"
        print(f"{status} {op}: Compatible={result['compatible']}, Preparable={result.get('preparable', 'N/A')}")
        if result.get('error'):
            print(f"    Error: {result['error']}")
    
    print("\n=== Device Support Test ===")
    device_results = tester.test_device_support(['CPU', 'CUDA', 'OpenCL'])
    for device, supported in device_results.items():
        status = "✓" if supported else "✗"
        print(f"{status} {device}: {supported}")
    
    print("\n=== Performance Benchmark ===")
    test_input = np.array([[1, 2], [3, 4]], dtype=np.float32)
    benchmark_results = tester.benchmark_execution(test_cases['Add'], [test_input], iterations=10)
    
    print(f"Direct execution time: {benchmark_results['direct_execution_time']:.4f}s")
    print(f"Prepared execution time: {benchmark_results['prepared_execution_time']:.4f}s")
    print(f"Speedup: {benchmark_results['speedup']:.2f}x")

# Run comprehensive tests
test_backend_comprehensive()

import onnx
from onnx import backend

def create_backend_test_suite(backend_class):
    """Create a test suite for backend validation."""
    
    class BackendTestSuite:
        def __init__(self):
            self.backend = backend_class
            
        def test_basic_compatibility(self):
            """Test basic backend functionality."""
            from onnx import helper, TensorProto
            
            # Create minimal model
            X = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1])
            Y = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1])
            identity_node = helper.make_node('Identity', ['input'], ['output'])
            graph = helper.make_graph([identity_node], 'identity', [X], [Y])
            model = helper.make_model(graph)
            
            # Test compatibility check
            assert hasattr(self.backend, 'is_compatible'), "Backend must implement is_compatible"
            
            # Test preparation
            if self.backend.is_compatible(model):
                rep = self.backend.prepare(model)
                assert hasattr(rep, 'run'), "BackendRep must implement run method"
                
        def test_device_support(self):
            """Test device support functionality."""
            assert hasattr(self.backend, 'supports_device'), "Backend must implement supports_device"
            
            # At minimum, should support CPU
            assert self.backend.supports_device('CPU'), "Backend should support CPU"
            
        def test_error_handling(self):
            """Test error handling for unsupported models."""
            from onnx import helper, TensorProto
            
            # Create model with unsupported operator
            X = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1])
            Y = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1])
            
            # Use a hypothetical unsupported operator
            unsupported_node = helper.make_node('UnsupportedOp', ['input'], ['output'])
            graph = helper.make_graph([unsupported_node], 'unsupported', [X], [Y])
            model = helper.make_model(graph)
            
            # Should either return False for is_compatible or raise appropriate exception
            try:
                compatible = self.backend.is_compatible(model)
                if compatible:
                    # If claiming compatibility, prepare should work
                    rep = self.backend.prepare(model)
            except Exception as e:
                # Acceptable if raises informative exception
                assert len(str(e)) > 0, "Exception should have descriptive message"
    
    return BackendTestSuite()

# Example usage
def validate_backend_implementation():
    """Validate that our backend implementation meets requirements."""
    
    test_suite = create_backend_test_suite(MyCustomBackend)
    
    try:
        test_suite.test_basic_compatibility()
        print("✓ Basic compatibility test passed")
    except Exception as e:
        print(f"✗ Basic compatibility test failed: {e}")
    
    try:
        test_suite.test_device_support()
        print("✓ Device support test passed")
    except Exception as e:
        print(f"✗ Device support test failed: {e}")
    
    try:
        test_suite.test_error_handling()
        print("✓ Error handling test passed")
    except Exception as e:
        print(f"✗ Error handling test failed: {e}")

# Validate our custom backend
validate_backend_implementation()