[Doc] update compression reference (#4667)

docs/source/reference/compression.rst

+Compression API Reference
+=========================
+
+Pruner
+------
+
+Please refer to :doc:`../compression/pruner`.
+
+Quantizer
+---------
+
+Please refer to :doc:`../compression/quantizer`.
+
+Pruning Speedup
+---------------
+
+.. autoclass:: nni.compression.pytorch.speedup.ModelSpeedup
+    :members:
+
+Quantization Speedup
+--------------------
+
+.. autoclass:: nni.compression.pytorch.quantization_speedup.ModelSpeedupTensorRT
+    :members:
+
+Compression Utilities
+---------------------
+
+.. autoclass:: nni.compression.pytorch.utils.sensitivity_analysis.SensitivityAnalysis
+    :members:
+
+.. autoclass:: nni.compression.pytorch.utils.shape_dependency.ChannelDependency
+    :members:
+
+.. autoclass:: nni.compression.pytorch.utils.shape_dependency.GroupDependency
+    :members:
+
+.. autoclass:: nni.compression.pytorch.utils.mask_conflict.ChannelMaskConflict
+    :members:
+
+.. autoclass:: nni.compression.pytorch.utils.mask_conflict.GroupMaskConflict
+    :members:
+
+.. autofunction:: nni.compression.pytorch.utils.counter.count_flops_params
+
+.. autofunction:: nni.algorithms.compression.v2.pytorch.utils.pruning.compute_sparsity
+
+Framework Related
+-----------------
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.base.Pruner
+    :members:
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.base.PrunerModuleWrapper
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.basic_pruner.BasicPruner
+    :members:
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.tools.DataCollector
+    :members:
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.tools.MetricsCalculator
+    :members:
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.tools.SparsityAllocator
+    :members:
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.base.BasePruningScheduler
+    :members:
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.tools.TaskGenerator
+    :members:
+
+.. autoclass:: nni.compression.pytorch.compressor.Quantizer
+    :members:
+
+.. autoclass:: nni.compression.pytorch.compressor.QuantizerModuleWrapper
+    :members:
+
+.. autoclass:: nni.compression.pytorch.compressor.QuantGrad
+    :members:

docs/source/reference/python_api/compression.rst

-Model Compression
-=================
-
-nni.algorithms.compression
---------------------------
-
-nni.compression
----------------

docs/source/reference/python_api_ref.rst

@@ -6,7 +6,7 @@ API Reference
 
     Hyperparameter Optimization <hpo>
     Neural Architecture Search <./python_api/nas>
-    Model Compression <./python_api/compression>
+    Model Compression <compression>
     Feature Engineering <./python_api/feature_engineering>
     Experiment <experiment>
     Others <./python_api/others>

docs/source/tutorials/pruning_quick_start_mnist.ipynb

@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Pruning Model\n\nUsing L1NormPruner pruning the model and generating the masks.\nUsually, pruners require original model and ``config_list`` as parameters.\nDetailed about how to write ``config_list`` please refer ...\n\nThis `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,\nexcept the layer named `fc3`, because `fc3` is `exclude`.\nThe final sparsity ratio for each layer is 50%. The layer named `fc3` will not be pruned.\n\n"
+        "## Pruning Model\n\nUsing L1NormPruner pruning the model and generating the masks.\nUsually, pruners require original model and ``config_list`` as parameters.\nDetailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.\n\nThis `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,\nexcept the layer named `fc3`, because `fc3` is `exclude`.\nThe final sparsity ratio for each layer is 50%. The layer named `fc3` will not be pruned.\n\n"
       ]
     },
     {

docs/source/tutorials/pruning_quick_start_mnist.py

@@ -50,7 +50,7 @@ for epoch in range(3):
 #
 # Using L1NormPruner pruning the model and generating the masks.
 # Usually, pruners require original model and ``config_list`` as parameters.
-# Detailed about how to write ``config_list`` please refer ...
+# Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
 #
 # This `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,
 # except the layer named `fc3`, because `fc3` is `exclude`.

docs/source/tutorials/pruning_quick_start_mnist.py.md5

-c87607b7befe8496829a8cb5a8632019
+bacea60d39b0445d01e3a233b1bfd249
\ No newline at end of file

docs/source/tutorials/pruning_quick_start_mnist.rst

@@ -102,9 +102,9 @@ If you are familiar with defining a model and training in pytorch, you can skip
 
  .. code-block:: none
 
-    Average test loss: 0.5603, Accuracy: 8270/10000 (83%)
-    Average test loss: 0.2395, Accuracy: 9289/10000 (93%)
-    Average test loss: 0.1660, Accuracy: 9527/10000 (95%)
+    Average test loss: 0.5876, Accuracy: 8158/10000 (82%)
+    Average test loss: 0.2501, Accuracy: 9217/10000 (92%)
+    Average test loss: 0.1786, Accuracy: 9486/10000 (95%)
 
 
 
@@ -116,7 +116,7 @@ Pruning Model
 
 Using L1NormPruner pruning the model and generating the masks.
 Usually, pruners require original model and ``config_list`` as parameters.
-Detailed about how to write ``config_list`` please refer ...
+Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
 
 This `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,
 except the layer named `fc3`, because `fc3` is `exclude`.
@@ -308,7 +308,7 @@ Because speed up will replace the masked big layers with dense small ones.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  38.705 seconds)
+   **Total running time of the script:** ( 1 minutes  33.096 seconds)
 
 
 .. _sphx_glr_download_tutorials_pruning_quick_start_mnist.py:

docs/source/tutorials/quantization_quick_start_mnist.ipynb

@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Quantizing Model\n\nInitialize a `config_list`.\n\n"
+        "## Quantizing Model\n\nInitialize a `config_list`.\nDetailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.\n\n"
       ]
     },
     {

docs/source/tutorials/quantization_quick_start_mnist.py

@@ -39,6 +39,7 @@ for epoch in range(3):
 # ----------------
 #
 # Initialize a `config_list`.
+# Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
 
 config_list = [{
     'quant_types': ['input', 'weight'],

docs/source/tutorials/quantization_quick_start_mnist.py.md5

-bcaf7880c66acfb20f3e5425730e21de
+6907e4d1a88c7f2f64e18e34a105e68e
\ No newline at end of file

docs/source/tutorials/quantization_quick_start_mnist.rst

@@ -68,21 +68,22 @@ If you are familiar with defining a model and training in pytorch, you can skip
 
  .. code-block:: none
 
-    Average test loss: 0.4891, Accuracy: 8504/10000 (85%)
-    Average test loss: 0.2644, Accuracy: 9179/10000 (92%)
-    Average test loss: 0.1953, Accuracy: 9414/10000 (94%)
+    Average test loss: 0.4877, Accuracy: 8541/10000 (85%)
+    Average test loss: 0.2618, Accuracy: 9191/10000 (92%)
+    Average test loss: 0.1626, Accuracy: 9543/10000 (95%)
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 38-42
+.. GENERATED FROM PYTHON SOURCE LINES 38-43
 
 Quantizing Model
 ----------------
 
 Initialize a `config_list`.
+Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 42-61
+.. GENERATED FROM PYTHON SOURCE LINES 43-62
 
 .. code-block:: default
 
@@ -112,11 +113,11 @@ Initialize a `config_list`.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 62-63
+.. GENERATED FROM PYTHON SOURCE LINES 63-64
 
 finetuning the model by using QAT
 
-.. GENERATED FROM PYTHON SOURCE LINES 63-71
+.. GENERATED FROM PYTHON SOURCE LINES 64-72
 
 .. code-block:: default
 
@@ -138,18 +139,20 @@ finetuning the model by using QAT
 
  .. code-block:: none
 
-    Average test loss: 0.1421, Accuracy: 9567/10000 (96%)
-    Average test loss: 0.1180, Accuracy: 9621/10000 (96%)
-    Average test loss: 0.1119, Accuracy: 9649/10000 (96%)
+    op_names ['relu1'] not found in model
+    op_names ['relu2'] not found in model
+    Average test loss: 0.1739, Accuracy: 9441/10000 (94%)
+    Average test loss: 0.1078, Accuracy: 9671/10000 (97%)
+    Average test loss: 0.0991, Accuracy: 9696/10000 (97%)
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 72-73
+.. GENERATED FROM PYTHON SOURCE LINES 73-74
 
 export model and get calibration_config
 
-.. GENERATED FROM PYTHON SOURCE LINES 73-78
+.. GENERATED FROM PYTHON SOURCE LINES 74-79
 
 .. code-block:: default
 
@@ -168,7 +171,7 @@ export model and get calibration_config
 
  .. code-block:: none
 
-    calibration_config:  {'conv1': {'weight_bits': 8, 'weight_scale': tensor([0.0034], device='cuda:0'), 'weight_zero_point': tensor([71.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': -0.4242129623889923, 'tracked_max_input': 2.821486711502075}, 'conv2': {'weight_bits': 8, 'weight_scale': tensor([0.0020], device='cuda:0'), 'weight_zero_point': tensor([112.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 13.904684066772461}}
+    calibration_config:  {'conv1': {'weight_bits': 8, 'weight_scale': tensor([0.0034], device='cuda:0'), 'weight_zero_point': tensor([75.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': -0.4242129623889923, 'tracked_max_input': 2.821486711502075}, 'conv2': {'weight_bits': 8, 'weight_scale': tensor([0.0018], device='cuda:0'), 'weight_zero_point': tensor([110.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 13.838628768920898}}
 
 
 
@@ -176,7 +179,7 @@ export model and get calibration_config
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  25.558 seconds)
+   **Total running time of the script:** ( 1 minutes  51.644 seconds)
 
 
 .. _sphx_glr_download_tutorials_quantization_quick_start_mnist.py:

docs/source/tutorials/sg_execution_times.rst

@@ -5,12 +5,12 @@
 
 Computation times
 =================
-**01:48.564** total execution time for **tutorials** files:
+**03:24.740** total execution time for **tutorials** files:
 
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_pruning_quick_start_mnist.py` (``pruning_quick_start_mnist.py``)           | 01:38.705 | 0.0 MB |
+| :ref:`sphx_glr_tutorials_quantization_quick_start_mnist.py` (``quantization_quick_start_mnist.py``) | 01:51.644 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_pruning_speed_up.py` (``pruning_speed_up.py``)                             | 00:09.859 | 0.0 MB |
+| :ref:`sphx_glr_tutorials_pruning_quick_start_mnist.py` (``pruning_quick_start_mnist.py``)           | 01:33.096 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorials_hello_nas.py` (``hello_nas.py``)                                           | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
@@ -20,9 +20,9 @@ Computation times
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorials_pruning_customize.py` (``pruning_customize.py``)                           | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_quantization_customize.py` (``quantization_customize.py``)                 | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_tutorials_pruning_speed_up.py` (``pruning_speed_up.py``)                             | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_quantization_quick_start_mnist.py` (``quantization_quick_start_mnist.py``) | 00:00.000 | 0.0 MB |
+| :ref:`sphx_glr_tutorials_quantization_customize.py` (``quantization_customize.py``)                 | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_tutorials_quantization_speed_up.py` (``quantization_speed_up.py``)                   | 00:00.000 | 0.0 MB |
 +-----------------------------------------------------------------------------------------------------+-----------+--------+

examples/tutorials/pruning_quick_start_mnist.py

@@ -50,7 +50,7 @@ for epoch in range(3):
 #
 # Using L1NormPruner pruning the model and generating the masks.
 # Usually, pruners require original model and ``config_list`` as parameters.
-# Detailed about how to write ``config_list`` please refer ...
+# Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
 #
 # This `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,
 # except the layer named `fc3`, because `fc3` is `exclude`.

examples/tutorials/quantization_quick_start_mnist.py

@@ -39,6 +39,7 @@ for epoch in range(3):
 # ----------------
 #
 # Initialize a `config_list`.
+# Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
 
 config_list = [{
     'quant_types': ['input', 'weight'],

nni/algorithms/compression/v2/pytorch/base/compressor.py

@@ -35,17 +35,16 @@ def _setattr(model: Module, name: str, module: Module):
 class Compressor:
     """
     The abstract base pytorch compressor.
+
+    Parameters
+    ----------
+    model
+        The model under compressed.
+    config_list
+        The config list used by compressor, usually specifies the 'op_types' or 'op_names' that want to compress.
     """
 
     def __init__(self, model: Optional[Module], config_list: Optional[List[Dict]]):
-        """
-        Parameters
-        ----------
-        model
-            The model under compressed.
-        config_list
-            The config list used by compressor, usually specifies the 'op_types' or 'op_names' that want to compress.
-        """
         self.is_wrapped = False
         if model is not None:
             self.reset(model=model, config_list=config_list)

nni/algorithms/compression/v2/pytorch/base/pruner.py

@@ -16,21 +16,22 @@ __all__ = ['Pruner']
 
 
 class PrunerModuleWrapper(Module):
-    def __init__(self, module: Module, module_name: str, config: Dict, pruner: Compressor):
-        """
-        Wrap a module to enable data parallel, forward method customization and buffer registeration.
+    """
+    Wrap a module to enable data parallel, forward method customization and buffer registeration.
+
+    Parameters
+    ----------
+    module
+        The module user wants to compress.
+    config
+        The configurations that users specify for compression.
+    module_name
+        The name of the module to compress, wrapper module shares same name.
+    pruner
+        The pruner used to calculate mask.
+    """
 
-        Parameters
-        ----------
-        module
-            The module user wants to compress.
-        config
-            The configurations that users specify for compression.
-        module_name
-            The name of the module to compress, wrapper module shares same name.
-        pruner
-            The pruner used to calculate mask.
-        """
+    def __init__(self, module: Module, module_name: str, config: Dict, pruner: Compressor):
         super().__init__()
         # origin layer information
         self.module = module

nni/algorithms/compression/v2/pytorch/pruning/tools/base.py

@@ -22,15 +22,14 @@ _logger = logging.getLogger(__name__)
 class DataCollector:
     """
     An abstract class for collect the data needed by the compressor.
+
+    Parameters
+    ----------
+    compressor
+        The compressor binded with this DataCollector.
     """
 
     def __init__(self, compressor: Compressor):
-        """
-        Parameters
-        ----------
-        compressor
-            The compressor binded with this DataCollector.
-        """
         self.compressor = compressor
 
     def reset(self):
@@ -242,42 +241,43 @@ class TrainerBasedDataCollector(DataCollector):
 class MetricsCalculator:
     """
     An abstract class for calculate a kind of metrics of the given data.
-    """
-    def __init__(self, dim: Optional[Union[int, List[int]]] = None,
-                 block_sparse_size: Optional[Union[int, List[int]]] = None):
-        """
-        Parameters
-        ----------
-        dim
-            The dimensions that corresponding to the under pruning weight dimensions in collected data.
-            None means one-to-one correspondence between pruned dimensions and data, which equal to set `dim` as all data dimensions.
-            Only these `dim` will be kept and other dimensions of the data will be reduced.
 
-            Example:
+    Parameters
+    ----------
+    dim
+        The dimensions that corresponding to the under pruning weight dimensions in collected data.
+        None means one-to-one correspondence between pruned dimensions and data, which equal to set `dim` as all data dimensions.
+        Only these `dim` will be kept and other dimensions of the data will be reduced.
 
-            If you want to prune the Conv2d weight in filter level, and the weight size is (32, 16, 3, 3) [out-channel, in-channel, kernal-size-1, kernal-size-2].
-            Then the under pruning dimensions is [0], which means you want to prune the filter or out-channel.
+        Example:
 
-                Case 1: Directly collect the conv module weight as data to calculate the metric.
-                Then the data has size (32, 16, 3, 3).
-                Mention that the dimension 0 of the data is corresponding to the under pruning weight dimension 0.
-                So in this case, `dim=0` will set in `__init__`.
+        If you want to prune the Conv2d weight in filter level, and the weight size is (32, 16, 3, 3) [out-channel, in-channel, kernal-size-1, kernal-size-2].
+        Then the under pruning dimensions is [0], which means you want to prune the filter or out-channel.
 
-                Case 2: Use the output of the conv module as data to calculate the metric.
-                Then the data has size (batch_num, 32, feature_map_size_1, feature_map_size_2).
-                Mention that the dimension 1 of the data is corresponding to the under pruning weight dimension 0.
-                So in this case, `dim=1` will set in `__init__`.
+            Case 1: Directly collect the conv module weight as data to calculate the metric.
+            Then the data has size (32, 16, 3, 3).
+            Mention that the dimension 0 of the data is corresponding to the under pruning weight dimension 0.
+            So in this case, `dim=0` will set in `__init__`.
 
-            In both of these two case, the metric of this module has size (32,).
-        block_sparse_size
-            This used to describe the block size a metric value represented. By default, None means the block size is ones(len(dim)).
-            Make sure len(dim) == len(block_sparse_size), and the block_sparse_size dimension position is corresponding to dim.
+            Case 2: Use the output of the conv module as data to calculate the metric.
+            Then the data has size (batch_num, 32, feature_map_size_1, feature_map_size_2).
+            Mention that the dimension 1 of the data is corresponding to the under pruning weight dimension 0.
+            So in this case, `dim=1` will set in `__init__`.
 
-            Example:
+        In both of these two case, the metric of this module has size (32,).
 
-            The under pruning weight size is (768, 768), and you want to apply a block sparse on dim=[0] with block size [64, 768],
-            then you can set block_sparse_size=[64]. The final metric size is (12,).
-        """
+    block_sparse_size
+        This used to describe the block size a metric value represented. By default, None means the block size is ones(len(dim)).
+        Make sure len(dim) == len(block_sparse_size), and the block_sparse_size dimension position is corresponding to dim.
+
+        Example:
+
+        The under pruning weight size is (768, 768), and you want to apply a block sparse on dim=[0] with block size [64, 768],
+        then you can set block_sparse_size=[64]. The final metric size is (12,).
+    """
+
+    def __init__(self, dim: Optional[Union[int, List[int]]] = None,
+                 block_sparse_size: Optional[Union[int, List[int]]] = None):
         self.dim = dim if not isinstance(dim, int) else [dim]
         self.block_sparse_size = block_sparse_size if not isinstance(block_sparse_size, int) else [block_sparse_size]
         if self.block_sparse_size is not None:
@@ -307,36 +307,35 @@ class MetricsCalculator:
 class SparsityAllocator:
     """
     An abstract class for allocate mask based on metrics.
+
+    Parameters
+    ----------
+    pruner
+        The pruner that binded with this `SparsityAllocator`.
+    dim
+        The under pruning weight dimensions, which metric size should equal to the under pruning weight size on these dimensions.
+        None means one-to-one correspondence between pruned dimensions and metric, which equal to set `dim` as all under pruning weight dimensions.
+        The mask will expand to the weight size depend on `dim`.
+
+        Example:
+
+        The under pruning weight has size (2, 3, 4), and `dim=1` means the under pruning weight dimension is 1.
+        Then the metric should have a size (3,), i.e., `metric=[0.9, 0.1, 0.8]`.
+        Assuming by some kind of `SparsityAllocator` get the mask on weight dimension 1 `mask=[1, 0, 1]`,
+        then the dimension mask will expand to the final mask `[[[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1]], [[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1]]]`.
+    block_sparse_size
+        This used to describe the block size a metric value represented. By default, None means the block size is ones(len(dim)).
+        Make sure len(dim) == len(block_sparse_size), and the block_sparse_size dimension position is corresponding to dim.
+
+        Example:
+
+        The metric size is (12,), and block_sparse_size=[64], then the mask will expand to (768,) at first before expand with `dim`.
+    continuous_mask
+        Inherit the mask already in the wrapper if set True.
     """
 
     def __init__(self, pruner: Compressor, dim: Optional[Union[int, List[int]]] = None,
                  block_sparse_size: Optional[Union[int, List[int]]] = None, continuous_mask: bool = True):
-        """
-        Parameters
-        ----------
-        pruner
-            The pruner that binded with this `SparsityAllocator`.
-        dim
-            The under pruning weight dimensions, which metric size should equal to the under pruning weight size on these dimensions.
-            None means one-to-one correspondence between pruned dimensions and metric, which equal to set `dim` as all under pruning weight dimensions.
-            The mask will expand to the weight size depend on `dim`.
-
-            Example:
-
-            The under pruning weight has size (2, 3, 4), and `dim=1` means the under pruning weight dimension is 1.
-            Then the metric should have a size (3,), i.e., `metric=[0.9, 0.1, 0.8]`.
-            Assuming by some kind of `SparsityAllocator` get the mask on weight dimension 1 `mask=[1, 0, 1]`,
-            then the dimension mask will expand to the final mask `[[[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1]], [[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1]]]`.
-        block_sparse_size
-            This used to describe the block size a metric value represented. By default, None means the block size is ones(len(dim)).
-            Make sure len(dim) == len(block_sparse_size), and the block_sparse_size dimension position is corresponding to dim.
-
-            Example:
-
-            The metric size is (12,), and block_sparse_size=[64], then the mask will expand to (768,) at first before expand with `dim`.
-        continuous_mask
-            Inherit the mask already in the wrapper if set True.
-        """
         self.pruner = pruner
         self.dim = dim if not isinstance(dim, int) else [dim]
         self.block_sparse_size = block_sparse_size if not isinstance(block_sparse_size, int) else [block_sparse_size]

nni/algorithms/compression/v2/pytorch/utils/pruning.py

@@ -200,6 +200,17 @@ def compute_sparsity(origin_model: Module, compact_model: Module, compact_model_
     The compact model is the origin model after pruning,
     and it may have different structure with origin_model cause of speed up.
 
+    Parameters
+    ----------
+    origin_model : torch.nn.Module
+        The original un-pruned model.
+    compact_model : torch.nn.Module
+        The model after speed up or original model.
+    compact_model_masks: Dict[str, Dict[str, Tensor]]
+        The masks applied on the compact model, if the original model have been speed up, this should be {}.
+    config_list : List[Dict]
+        The config_list used by pruning the original model.
+
     Returns
     -------
     Tuple[List[Dict], List[Dict], List[Dict]]