hls4ml.model.optimizer package
Subpackages
- hls4ml.model.optimizer.passes package
- Submodules
- hls4ml.model.optimizer.passes.bn_fuse module
- hls4ml.model.optimizer.passes.convert_to_channels_last module
- hls4ml.model.optimizer.passes.expand_layer_group module
- hls4ml.model.optimizer.passes.fuse_biasadd module
- hls4ml.model.optimizer.passes.hgq_proxy_model module
- hls4ml.model.optimizer.passes.infer_precision module
- hls4ml.model.optimizer.passes.multi_dense module
- hls4ml.model.optimizer.passes.nop module
- hls4ml.model.optimizer.passes.qkeras module
- hls4ml.model.optimizer.passes.seperable_to_dw_conv module
- hls4ml.model.optimizer.passes.stamp module
- hls4ml.model.optimizer.passes.transpose_opt module
- Module contents
Submodules
hls4ml.model.optimizer.optimizer module
- class hls4ml.model.optimizer.optimizer.ConfigurableOptimizerPass
Bases:
OptimizerPass
An optimizer that can be configured.
Existing instances of this class in the registry can be configured with the configure() method. Multiple instances with different configuration can co-exist if registered with different names.
- configure(**kwargs)
- get_config()
- class hls4ml.model.optimizer.optimizer.GlobalOptimizerPass
Bases:
OptimizerPass
Global optimizer that matches on every node in the model graph.
- class hls4ml.model.optimizer.optimizer.LayerOptimizerPass(name, layer_class, transform)
Bases:
WrappedOptimizerPass
An wrapper optimizer specific to a layer class.
Commonly used by backends to add extra initialization to a layer instance.
- class hls4ml.model.optimizer.optimizer.ModelOptimizerPass(name, transform)
Bases:
OptimizerPass
A special optimizer that works with the model itself.
Examples include writing the model to C++/HLS.
- transform(model)
Transformation to apply if matching was successful.
Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).
- Parameters:
model (ModelGraph) – Model to optimize
node (Layer) – The matched node in the model graph.
- class hls4ml.model.optimizer.optimizer.OptimizerPass
Bases:
object
Base optimizer class from which all other optimizer types are derived.
- classmethod get_name()
- match(node)
Predicate to match on a given node.
- Parameters:
node (Layer) – Node in the model graph to try matching the optimizer on.
- name = None
- transform(model, node)
Transformation to apply if matching was successful.
Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).
- Parameters:
model (ModelGraph) – Model to optimize
node (Layer) – The matched node in the model graph.
- class hls4ml.model.optimizer.optimizer.WrappedOptimizerPass(name, condition, transform)
Bases:
OptimizerPass
An optimizer class created by wrapping a function call.
Users should generally not create any wrapped optimizer passes manually.
- get_name()
- match(node)
Predicate to match on a given node.
- Parameters:
node (Layer) – Node in the model graph to try matching the optimizer on.
- transform(model, node)
Transformation to apply if matching was successful.
Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).
- Parameters:
model (ModelGraph) – Model to optimize
node (Layer) – The matched node in the model graph.
- hls4ml.model.optimizer.optimizer.extract_optimizers_from_object(clazz)
- hls4ml.model.optimizer.optimizer.extract_optimizers_from_path(opt_path, module_path, initializer=None)
- hls4ml.model.optimizer.optimizer.get_available_passes()
Return the list of all registered optimizer passes.
- Returns:
List of registered passes.
- Return type:
list
- hls4ml.model.optimizer.optimizer.get_backend_passes(backend)
Returns the list of optimizer passes belonging to a backend
- Parameters:
backend (str) – Name of the backend.
- Returns:
List of optimizer names registered with the given backend.
- Return type:
list
- hls4ml.model.optimizer.optimizer.get_optimizer(name)
Return the optimizer instance registered with the given name.
- Parameters:
name (str) – Name of the optimizer in the registry.
- Raises:
Exception – If the optimizer with the given name is not found in the registry.
- Returns:
The optimizer from the registry.
- Return type:
- hls4ml.model.optimizer.optimizer.layer_optimizer(layer)
Decorator to turn a function into the optimization pass.
Example:
@layer_optimizer(MyLayer) def init_mylayer(self, layer): layer.set_attr('new_attribute', 'some_value')
- Parameters:
layer (_type_) – _description_
- hls4ml.model.optimizer.optimizer.model_optimizer()
Decorator to turn a function into a model optimizer.
- hls4ml.model.optimizer.optimizer.optimize_model(model, passes)
Optimize a given model with the given passes.
The passes are attempted until all passes no longer match or no changes to the model graph occur.
- Parameters:
model (ModelGraph) – The model to optimize.
passes (list) – List of passes to apply.
- Returns:
The set of applied passes (the passes that matched the predicate).
- Return type:
set
- hls4ml.model.optimizer.optimizer.optimizer_pass(condition)
- hls4ml.model.optimizer.optimizer.register_pass(name, opt_cls, backend=None)
Register a new optimizer pass.
- Parameters:
name (str) – Name of the optimizer
opt_cls (class) – The class of the optimizer.
backend (str, optional) – Optional backend to register the optimizer to. If not None, the name of the backend will be appended to the name of the registered flow. Defaults to None.
- Raises:
Exception – If the optimization pass has already been registered with the given name.
- Returns:
The name of the registered optimizer.
- Return type:
str