Optimization Flows

class OptimizerPass:
    """Base optimizer class from which all other optimizer types are derived."""
    
    name = None
    
    def __init__(self):
        pass
    
    def match(self, node):
        """Predicate to match on a given node.
        
        Args:
            node (Layer): Node in the model graph to try matching
        """
        raise NotImplementedError
    
    def transform(self, model, node):
        """Transformation to apply if matching was successful.
        
        Should return a boolean indicating if the graph was altered.
        
        Args:
            model (ModelGraph): Model to optimize
            node (Layer): The matched node in the model graph
        """
        raise NotImplementedError

class FuseBatchNormalization(OptimizerPass):
    """Merge BatchNormalization layer with Dense or Conv layer."""
    
    def match(self, node):
        prev_node = node.get_input_node()
        basic_match = (
            isinstance(node, BatchNormalization) and
            isinstance(prev_node, (Dense, Conv1D, Conv2D)) and
            isinstance(prev_node.get_output_variable().type.precision,
                      UnspecifiedPrecisionType)
        )
        if basic_match:
            # Additional compatibility checks
            s0 = prev_node.weights['weight'].data_unquantized
            b0 = prev_node.weights['bias'].data_unquantized
            s1 = node.weights['scale'].data_unquantized
            b1 = node.weights['bias'].data_unquantized
            
            scale_compatible = (
                (prev_node.get_attr('weight_quantizer') is None and
                 node.get_attr('scale_quantizer') is None) or
                ((s0 == np.ones_like(s0)).all() and
                 prev_node.get_attr('weight_quantizer') is None) or
                ((s1 == np.ones_like(s1)).all() and
                 node.get_attr('scale_quantizer') is None)
            )
            
            bias_compatible = (
                (prev_node.get_attr('bias_quantizer') is None and
                 node.get_attr('bias_quantizer') is None) or
                ((b0 == np.zeros_like(b0)).all() and
                 prev_node.get_attr('bias_quantizer') is None) or
                ((b1 == np.zeros_like(b1)).all() and
                 node.get_attr('bias_quantizer') is None)
            )
            
            return scale_compatible and bias_compatible
        else:
            return False
    
    def transform(self, model, node):
        """Fuse weight and bias with BN values."""
        parent_node = node.get_input_node()
        parent_map = parent_node.get_output_use_map()
        
        # Only fuse if parent has single consumer
        if len(parent_map[parent_node.outputs[0]]) > 1:
            return False
        
        parent_weight = parent_node.weights['weight']
        parent_bias = parent_node.weights['bias']
        bn_scale = node.weights['scale']
        bn_bias = node.weights['bias']
        
        # Compute fused parameters
        fused_weight = bn_scale.data * parent_weight.data
        fused_bias = bn_scale.data * parent_bias.data + bn_bias.data
        
        # Update parent node with fused parameters
        parent_node.add_weights_variable(
            name='weight', var_name='w{index}',
            data=fused_weight, quantizer=w_quantizer
        )
        parent_node.add_weights_variable(
            name='bias', var_name='b{index}',
            data=fused_bias, quantizer=b_quantizer
        )
        
        # Remove BN node from graph
        model.remove_node(node)
        return True

class GlobalOptimizerPass(OptimizerPass):
    """Global optimizer that matches on every node in the model graph."""
    
    def match(self, node):
        return True  # Match everything

class LayerOptimizerPass(WrappedOptimizerPass):
    """An optimizer specific to a layer class.
    
    Commonly used by backends to add extra initialization.
    """
    
    def __init__(self, name, layer_class, transform):
        super().__init__(
            name,
            lambda node: isinstance(node, layer_class),
            transform
        )
        self.layer_class = layer_class

class ModelOptimizerPass(OptimizerPass):
    """A special optimizer that works with the model itself.
    
    Examples include writing the model to C++/HLS.
    """
    
    def __init__(self, name, transform):
        self.name = name
        self.transform_func = transform
    
    def transform(self, model):
        retval = self.transform_func(model)
        return retval if retval is not None else False

class ConfigurableOptimizerPass(OptimizerPass):
    """An optimizer that can be configured.
    
    Existing instances can be configured with the configure() method.
    """
    
    def configure(self, **kwargs):
        for key, value in kwargs.items():
            setattr(self, key, value)
    
    def get_config(self):
        attrs = vars(self)
        return attrs.copy()

# Layer-specific optimizer
@layer_optimizer(MyLayer)
def init_mylayer(layer):
    layer.set_attr('new_attribute', 'some_value')

# Model-level optimizer
@model_optimizer()
def write_model(model):
    model.config.backend.write(model)

# Custom condition optimizer
@optimizer_pass(lambda node: node.get_attr('some_flag'))
def optimize_flagged(node):
    # Transform node
    pass

class Flow:
    """Collection of optimizers with optional dependencies."""
    
    def __init__(self, name, optimizers, requires=None):
        """Create a new flow.
        
        Args:
            name (str): Unique name of the flow
            optimizers (list): List of optimizer pass names
            requires (list, optional): List of flows that must run first
        """
        self.name = name
        if optimizers is None:
            self._optimizers = []
        else:
            self._optimizers = optimizers
        
        if requires is None:
            self.requires = []
        else:
            self.requires = requires
    
    @property
    def optimizers(self):
        return self._optimizers

class DynamicFlow(Flow):
    """A dynamically updated flow.
    
    Gets optimizer list by calling a function. Useful for representing
    all available optimizers of a certain type.
    """
    
    def __init__(self, name, optimizer_func, requires=None):
        """Create a new dynamic flow.
        
        Args:
            name (str): Unique name
            optimizer_func (callable): Function to get optimizer list
            requires (list, optional): Required flows
        """
        self.name = name
        self._optimizer_func = optimizer_func
        self._added_optimizers = set()
        self._removed_optimizers = set()
        
        if requires is None:
            self.requires = []
        else:
            self.requires = requires
    
    @property
    def optimizers(self):
        optimizers = self._optimizer_func()
        optimizers.extend(self._added_optimizers)
        optimizers = [o for o in optimizers 
                     if o not in self._removed_optimizers]
        return optimizers

def register_flow(name, optimizers, requires=None, backend=None):
    """Create a flow and add it to the registry.
    
    Args:
        name (str): Flow name
        optimizers (list): List of optimizer pass names
        requires (list, optional): Required flows
        backend (str, optional): Backend name to prefix
    
    Returns:
        str: The registered flow name
    """
    if backend is not None and not name.startswith(backend.lower()):
        name = backend.lower() + ':' + name
    
    if name in flow_map:
        raise Exception(f'Flow {name} already registered')
    
    if callable(optimizers):
        flow = DynamicFlow(name, optimizer_func=optimizers, 
                          requires=requires)
    else:
        flow = Flow(name, optimizers=optimizers, requires=requires)
    
    flow_map[name] = flow
    return name

def apply_flow(self, flow, reapply='single'):
    """Apply a flow (collection of optimizers).
    
    Args:
        flow (str): Name of the flow to apply
        reapply (str, optional): How to handle already-applied flows:
            - 'all': Apply flow and all requirements
            - 'single': Apply only the flow, skip applied requirements
            - 'none': Skip if already applied
            Defaults to 'single'
    """
    assert reapply in ['all', 'single', 'none']
    
    if reapply == 'all':
        applied_flows = {}
    elif reapply == 'single':
        applied_flows = self._all_applied_flows()
        applied_flows.pop(flow, None)
    else:  # reapply == 'none'
        applied_flows = self._all_applied_flows()
        if flow in applied_flows:
            return
    
    self._applied_flows.append(applied_flows)
    self._apply_sub_flow(flow, applied_flows)

def _apply_sub_flow(self, flow_name, applied_flows):
    """Apply a flow and its dependencies recursively."""
    if flow_name in applied_flows:
        return
    
    flow = get_flow(flow_name)
    
    # Apply required flows first
    for sub_flow in flow.requires:
        if sub_flow not in applied_flows.keys():
            self._apply_sub_flow(sub_flow, applied_flows)
    
    # Apply this flow's optimizers
    if len(flow.optimizers) > 0:
        applied_passes = optimize_model(self, flow.optimizers)
    else:
        applied_passes = set()
    
    applied_flows[flow.name] = applied_passes

def optimize_model(model, passes):
    """Optimize a model with the given passes.
    
    Passes are attempted until all passes no longer match or no changes occur.
    
    Args:
        model (ModelGraph): The model to optimize
        passes (list): List of pass names to apply
    
    Returns:
        set: The set of applied passes
    """
    optimizers = {opt_pass: get_optimizer(opt_pass) 
                 for opt_pass in passes}
    applied_passes = set()
    optimization_done = False
    
    while not optimization_done:
        for opt_name, opt in optimizers.items():
            # Handle ModelOptimizerPass
            if isinstance(opt, ModelOptimizerPass):
                if opt_name not in applied_passes:
                    res = opt.transform(model)
                    if res:
                        applied_passes.add(opt_name)
                continue
            
            # Try to match and transform each node
            for node in model.graph.values():
                if opt.match(node):
                    res = opt.transform(model, node)
                    applied_passes.add(opt_name)
                    if res:  # Graph was modified
                        break
            else:
                # No match found for this optimizer
                continue
            
            # Graph was modified, restart outer loop
            break
        else:
            # All optimizers checked, no modifications
            optimization_done = True
    
    return applied_passes

class FuseBatchNormalization(OptimizerPass):
    def match(self, node):
        # Check if BN follows Dense/Conv
        prev_node = node.get_input_node()
        return (
            isinstance(node, BatchNormalization) and
            isinstance(prev_node, (Dense, Conv1D, Conv2D))
        )
    
    def transform(self, model, node):
        parent = node.get_input_node()
        # Compute fused parameters
        fused_weight = bn_scale.data * parent_weight.data
        fused_bias = bn_scale.data * parent_bias.data + bn_bias.data
        # Update parent and remove BN node
        model.remove_node(node)
        return True

class ExpandLayer(OptimizerPass):
    def match(self, node):
        return isinstance(node, ComplexLayer)
    
    def transform(self, model, node):
        # Create simpler sub-layers
        layer1 = model.make_node('SubLayer1', ...)
        layer2 = model.make_node('SubLayer2', ...)
        
        # Replace complex layer with sequence
        model.split_node(node, layer1, layer2)
        return True

class InferPrecision(GlobalOptimizerPass):
    def transform(self, model, node):
        # Infer output precision from input and weights
        inp_precision = node.get_input_variable().type.precision
        weight_precision = node.weights['weight'].type.precision
        
        # Calculate required output precision
        output_precision = calculate_precision(
            inp_precision, weight_precision
        )
        
        # Update node's output type
        node.set_attr('result_t', 
                     NamedType(f'{node.name}_result_t', 
                              output_precision))
        return False  # Don't break optimization loop

class VivadoOptimization(OptimizerPass):
    def match(self, node):
        return (
            isinstance(node, Conv2D) and
            model.config.backend.name == 'Vivado'
        )
    
    def transform(self, model, node):
        # Add Vivado-specific attributes
        node.set_attr('implementation', 'LineBuffer')
        node.set_attr('partition_mode', 'cyclic')
        return False

def register_pass(name, opt_cls, backend=None):
    """Register a new optimizer pass.
    
    Args:
        name (str): Name of the optimizer
        opt_cls (class): The optimizer class
        backend (str, optional): Backend to register to
    
    Returns:
        str: The registered optimizer name
    """
    if backend is not None and not name.startswith(backend.lower() + ':'):
        name = backend.lower() + ':' + name
    
    if name in optimizer_map:
        raise Exception(f'Optimization pass {name} already registered')
    
    if inspect.isclass(opt_cls):
        opt = opt_cls()
    else:
        opt = opt_cls
    
    optimizer_map[name] = opt
    return name

def extract_optimizers_from_path(opt_path, module_path, initializer=None):
    """Extract optimizer passes from a directory."""
    optimizers = {}
    
    for module in os.listdir(opt_path):
        if module == '__init__.py' or module[-3:] != '.py':
            continue
        
        try:
            lib = importlib.import_module(module_path + module[:-3])
            
            # Look for register function
            if 'register_' + module[:-3] in lib.__dict__:
                opt_init_func = lib.__dict__['register_' + module[:-3]]
                opt_init_func(initializer)
            else:
                # Auto-discover OptimizerPass classes
                for func in list(lib.__dict__.values()):
                    if (inspect.isclass(func) and 
                        issubclass(func, OptimizerPass) and
                        func.__module__ == lib.__name__):
                        
                        func_instance = func()
                        optimizers[func_instance.get_name()] = func_instance
        
        except ImportError as e:
            print(f'WARN: Unable to import optimizer from {module}: {e}')
            continue
    
    return optimizers

# Define optimization passes
class MyCustomPass(OptimizerPass):
    def match(self, node):
        return isinstance(node, MyLayerType)
    
    def transform(self, model, node):
        # Custom transformation logic
        node.set_attr('optimized', True)
        return False

# Register the pass
register_pass('my_custom_pass', MyCustomPass, backend='Vivado')

# Create a flow
register_flow(
    'my_optimization_flow',
    optimizers=[
        'infer_precision',
        'my_custom_pass',
        'vivado:apply_templates'
    ],
    requires=['vivado:init_layers'],
    backend='Vivado'
)

# Apply the flow
model.apply_flow('vivado:my_optimization_flow')