Framework Related

Pruner

class nni.algorithms.compression.v2.pytorch.base.Pruner(model, config_list)

The abstract class for pruning algorithm. Inherit this class and implement the _reset_tools to customize a pruner.

compress()

Returns

Return the wrapped model and mask.

Return type

Tuple[Module, Dict]

export_model(model_path, mask_path=None)

Export pruned model weights, masks and onnx model(optional)

Parameters

• model_path (str) – Path to save pruned model state_dict. The weight and bias have already multiplied the masks.

• mask_path (Optional[str]) – Path to save mask dict.

get_modules_wrapper()

Returns

An ordered dict, key is the name of the module, value is the wrapper of the module.

Return type

OrderedDict[str, PrunerModuleWrapper]

get_origin2wrapped_parameter_name_map()

Get the name mapping of parameters from original model to wrapped model.

Returns

Return a dict {original_model_parameter_name: wrapped_model_parameter_name}

Return type

Dict[str, str]

load_masks(masks)

Load an exist masks on the wrapper. You can train the model with an exist masks after load the masks.

Parameters

masks (Dict[str, Dict[str, Tensor]]) – The masks dict with format {‘op_name’: {‘weight’: mask, ‘bias’: mask}}.

show_pruned_weights(dim=0)

Log the simulated prune sparsity.

Parameters

dim (int) – The pruned dim.

PrunerModuleWrapper

class nni.algorithms.compression.v2.pytorch.base.PrunerModuleWrapper(module, module_name, config)

Wrap a module to enable data parallel, forward method customization and buffer registeration.

Parameters

• module (Module) – The module user wants to compress.

• config (Dict) – The configurations that users specify for compression.

• module_name (str) – The name of the module to compress, wrapper module shares same name.

BasicPruner

class nni.algorithms.compression.v2.pytorch.pruning.basic_pruner.BasicPruner(model, config_list)

compress()

Used to generate the mask. Pruning process is divided in three stages. self.data_collector collect the data used to calculate the specify metric. self.metrics_calculator calculate the metric and self.sparsity_allocator generate the mask depend on the metric.

Returns

Return the wrapped model and mask.

Return type

Tuple[Module, Dict]

reset_tools()

This function is used to reset self.data_collector, self.metrics_calculator and self.sparsity_allocator. The subclass needs to implement this function to complete the pruning process. See compress() to understand how NNI use these three part to generate mask for the bound model.

DataCollector

class nni.algorithms.compression.v2.pytorch.pruning.tools.DataCollector(compressor)

An abstract class for collect the data needed by the compressor.

Parameters

compressor (Pruner) – The compressor binded with this DataCollector.

collect()

Collect the compressor needed data, i.e., module weight, the output of activation function.

Returns

Usually has format like {module_name: tensor_type_data}.

Return type

Dict

reset()

Reset the DataCollector.

MetricsCalculator

class nni.algorithms.compression.v2.pytorch.pruning.tools.MetricsCalculator(scalers=None)

An abstract class for calculate a kind of metrics of the given data.

Parameters

scalers (Dict[str, Dict[str, Scaling]] | Scaling | None) – Scaler is used to scale the metrics’ size. It scaling metric to the same size as the shrinked mask in the sparsity allocator. If you want to use different scalers for different pruning targets in different modules, please use a dict {module_name: {target_name: scaler}}. If allocator meets an unspecified module name, it will try to use scalers[‘_default’][target_name] to scale its mask. If allocator meets an unspecified target name, it will try to use scalers[module_name][‘_default’] to scale its mask. Passing in a scaler instead of a dict of scalers will be treated as passed in {‘_default’: {‘_default’: scalers}}. Passing in None means no need to scale.

calculate_metrics(data)

Parameters

data (Dict) – A dict handle the data used to calculate metrics. Usually has format like {module_name: tensor_type_data}.

Returns

The key is the layer_name, value is the metric. Note that the metric has the same size with the data size on dim.

Return type

Dict[str, Tensor]

SparsityAllocator

class nni.algorithms.compression.v2.pytorch.pruning.tools.SparsityAllocator(pruner, scalers=None, continuous_mask=True)

A base class for allocating mask based on metrics.

Parameters

• pruner (Pruner) – The pruner that binded with this SparsityAllocator.

• scalers (Dict[str, Dict[str, Scaling]] | Scaling | None) – Scaler is used to scale the masks’ size. It shrinks the mask of the same size as the pruning target to the same size as the metric, or expands the mask of the same size as the metric to the same size as the pruning target. If you want to use different scalers for different pruning targets in different modules, please use a dict {module_name: {target_name: scaler}}. If allocator meets an unspecified module name, it will try to use scalers[‘_default’][target_name] to scale its mask. If allocator meets an unspecified target name, it will try to use scalers[module_name][‘_default’] to scale its mask. Passing in a scaler instead of a dict of scalers will be treated as passed in {‘_default’: {‘_default’: scalers}}. Passing in None means no need to scale.

• continuous_mask (bool) – If set True, the part that has been masked will be masked first. If set False, the part that has been masked may be unmasked due to the increase of its corresponding metric.

common_target_masks_generation(metrics)

Generate masks for metrics-dependent targets.

Parameters

metrics (Dict[str, Tensor]) – The format is {module_name: weight_metric}. The metric of weight usually has the same size with shrinked mask.

Returns

The format is {module_name: {target_name: mask}}. Return the masks of the same size as its target.

Return type

Dict[str, Dict[str, Tensor]]

generate_sparsity(metrics)

The main function of SparsityAllocator, generate a set of masks based on the given metrics.

Parameters

metrics (Dict) – A metric dict with format {module_name: weight_metric}

Returns

The masks format is {module_name: {target_name: mask}}.

Return type

Dict[str, Dict[str, Tensor]]

special_target_masks_generation(masks)

Some pruning targets’ mask generation depends on other targets, i.e., bias mask depends on weight mask. This function is used to generate these masks, and it be called at the end of generate_sparsity.

Parameters

masks (Dict[str, Dict[str, Tensor]]) – The format is {module_name: {target_name: mask}}. It is usually the return value of common_target_masks_generation.

BasePruningScheduler

class nni.algorithms.compression.v2.pytorch.base.BasePruningScheduler

compress()

The pruning schedule main loop.

generate_task()

Returns

Return the next pruning task.

Return type

Optional[Task]

get_best_result()

Returns

Return the task result that has the best performance, inculde task id, the compact model, the masks on the compact model, score and config list used in this task.

Return type

Tuple[int, Module, Dict[str, Dict[str, Tensor]], float, List[Dict]]

pruning_one_step(task)

Pruning the model defined in task.

Parameters

task (Task) – The pruning task in this step.

Returns

Return the result of the task in this step.

Return type

TaskResult

record_task_result(task_result)

Parameters

task_result (TaskResult) – The result of the task

TaskGenerator

class nni.algorithms.compression.v2.pytorch.pruning.tools.TaskGenerator(origin_model, origin_masks={}, origin_config_list=[], log_dir='.', keep_intermediate_result=False)

This class used to generate config list for pruner in each iteration.

Parameters

• origin_model (Optional[Module]) – The origin unwrapped pytorch model to be pruned.

• origin_masks (Optional[Dict[str, Dict[str, Tensor]]]) – The pre masks on the origin model. This mask maybe user-defined or maybe generate by previous pruning.

• origin_config_list (Optional[List[Dict]]) – The origin config list provided by the user. Note that this config_list is directly config the origin model. This means the sparsity provided by the origin_masks should also be recorded in the origin_config_list.

• log_dir (Union[str, Path]) – The log directory use to saving the task generator log.

• keep_intermediate_result (bool) – If keeping the intermediate result, including intermediate model and masks during each iteration.

get_best_result()

Returns

If self._best_task_id is not None, return best task id, best compact model, masks on the compact model, score, config list used in this task.

Return type

Optional[Tuple[int, Module, Dict[str, Dict[str, Tensor]], float, List[Dict]]]

next()

Returns

Return the next task from pending tasks.

Return type

Optional[Task]

receive_task_result(task_result)

Parameters

task_result (TaskResult) – The result of the task.

Quantizer

class nni.compression.pytorch.compressor.Quantizer(model, config_list, optimizer=None, dummy_input=None)

Base quantizer for pytorch quantizer

export_model(model_path, calibration_path=None, onnx_path=None, input_shape=None, device=None)

Export quantized model weights and calibration parameters

Parameters

• model_path (str) – path to save quantized model weight

• calibration_path (str) – (optional) path to save quantize parameters after calibration

• onnx_path (str) – (optional) path to save onnx model

• input_shape (list or tuple) – input shape to onnx model

• device (torch.device) – device of the model, used to place the dummy input tensor for exporting onnx file. the tensor is placed on cpu if `device` is None

Return type

Dict

export_model_save(model, model_path, calibration_config=None, calibration_path=None, onnx_path=None, input_shape=None, device=None)

This method helps save pytorch model, calibration config, onnx model in quantizer.

Parameters

• model (pytorch model) – pytorch model to be saved

• model_path (str) – path to save pytorch

• calibration_config (dict) – (optional) config of calibration parameters

• calibration_path (str) – (optional) path to save quantize parameters after calibration

• onnx_path (str) – (optional) path to save onnx model

• input_shape (list or tuple) – input shape to onnx model

• device (torch.device) – device of the model, used to place the dummy input tensor for exporting onnx file. the tensor is placed on cpu if `device` is None

find_conv_bn_patterns(model, dummy_input)

Find all Conv-BN patterns, used for batch normalization folding

Parameters

• model (torch.nn.Module) – model to be analyzed.

• dummy_input (tupel of torch.tensor) – inputs to the model, used for generating the torchscript

fold_bn(*inputs, wrapper)

Simulate batch normalization folding in the training graph. Folded weight and bias are returned for the following operations.

Parameters

• inputs (tuple of torch.Tensor) – inputs for the module

• wrapper (QuantizerModuleWrapper) – the wrapper for origin module

Return type

Tuple of torch.Tensor

load_calibration_config(calibration_config)

This function aims to help quantizer set quantization parameters by loading from a calibration_config which is exported by other quantizer or itself. The main usage of this function is helping quantize aware training quantizer set appropriate initial parameters so that the training process will be much more flexible and converges quickly. What’s more, it can also enable quantizer resume quantization model by loading parameters from config.

Parameters

calibration_config (dict) – dict which saves quantization parameters, quantizer can export itself calibration config. eg, calibration_config = quantizer.export_model(model_path, calibration_path)

quantize_input(inputs, wrapper, **kwargs)

quantize should overload this method to quantize input. This method is effectively hooked to forward() of the model.

Parameters

• inputs (Tensor) – inputs that needs to be quantized

• wrapper (QuantizerModuleWrapper) – the wrapper for origin module

quantize_output(output, wrapper, **kwargs)

quantize should overload this method to quantize output. This method is effectively hooked to forward() of the model.

Parameters

• output (Tensor) – output that needs to be quantized

• wrapper (QuantizerModuleWrapper) – the wrapper for origin module

quantize_weight(wrapper, **kwargs)

quantize should overload this method to quantize weight. This method is effectively hooked to forward() of the model.

Parameters

wrapper (QuantizerModuleWrapper) – the wrapper for origin module

record_shape(model, dummy_input)

Record input/output’s shapes of each module to be quantized

Parameters

• model (torch.nn.Module) – model to be recorded.

• dummy_input (tupel of torch.tensor) – inputs to the model.

QuantizerModuleWrapper

class nni.compression.pytorch.compressor.QuantizerModuleWrapper(module, module_name, module_type, config, quantizer, bn_module=None)

QuantGrad

class nni.compression.pytorch.compressor.QuantGrad(*args, **kwargs)

Base class for overriding backward function of quantization operation.

classmethod get_bits_length(config, quant_type)

Get bits for quantize config

Parameters

• config (Dict) – the configuration for quantization

• quant_type (str) – quant type

Returns

n-bits for quantization configuration

Return type

int

static quant_backward(tensor, grad_output, quant_type, scale, zero_point, qmin, qmax)

This method should be overrided by subclass to provide customized backward function, default implementation is Straight-Through Estimator

Parameters

• tensor (Tensor) – input of quantization operation

• grad_output (Tensor) – gradient of the output of quantization operation

• scale (Tensor) – the type of quantization, it can be QuantType.INPUT, QuantType.WEIGHT, QuantType.OUTPUT, you can define different behavior for different types.

• zero_point (Tensor) – zero_point for quantizing tensor

• qmin (Tensor) – quant_min for quantizing tensor

• qmax (Tensor) – quant_max for quantizng tensor

Returns

gradient of the input of quantization operation

Return type

tensor