Pruning schedule supports fpgm (#3110)

nni/algorithms/compression/pytorch/pruning/admm_pruner.py

@@ -4,6 +4,7 @@
 import logging
 import torch
 from schema import And, Optional
+import copy
 
 from nni.compression.pytorch.utils.config_validation import CompressorSchema
 from .constants import MASKER_DICT
@@ -53,7 +54,7 @@ class ADMMPruner(OneshotPruner):
     row : float
         Penalty parameters for ADMM training.
     base_algo : str
-        Base pruning algorithm. `level`, `l1` or `l2`, by default `l1`. Given the sparsity distribution among the ops,
+        Base pruning algorithm. `level`, `l1`, `l2` or `fpgm`, by default `l1`. Given the sparsity distribution among the ops,
         the assigned `base_algo` is used to decide which filters/channels/weights to prune.
 
     """
@@ -87,7 +88,7 @@ class ADMMPruner(OneshotPruner):
                 Optional('op_types'): [str],
                 Optional('op_names'): [str],
             }], model, _logger)
-        elif self._base_algo in ['l1', 'l2']:
+        elif self._base_algo in ['l1', 'l2', 'fpgm']:
             schema = CompressorSchema([{
                 'sparsity': And(float, lambda n: 0 < n < 1),
                 'op_types': ['Conv2d'],
@@ -96,7 +97,7 @@ class ADMMPruner(OneshotPruner):
 
         schema.validate(config_list)
 
-    def _projection(self, weight, sparsity):
+    def _projection(self, weight, sparsity, wrapper):
         '''
         Return the Euclidean projection of the weight matrix according to the pruning mode.
 
@@ -106,31 +107,17 @@ class ADMMPruner(OneshotPruner):
             original matrix
         sparsity : float
             the ratio of parameters which need to be set to zero
+        wrapper: PrunerModuleWrapper
+            layer wrapper of this layer
 
         Returns
         -------
         tensor
             the projected matrix
         '''
-        w_abs = weight.abs()
-        if self._base_algo == 'level':
-            k = int(weight.numel() * sparsity)
-            if k == 0:
-                mask_weight = torch.ones(weight.shape).type_as(weight)
-            else:
-                threshold = torch.topk(w_abs.view(-1), k, largest=False)[0].max()
-                mask_weight = torch.gt(w_abs, threshold).type_as(weight)
-        elif self._base_algo in ['l1', 'l2']:
-            filters = weight.size(0)
-            num_prune = int(filters * sparsity)
-            if filters < 2 or num_prune < 1:
-                mask_weight = torch.ones(weight.size()).type_as(weight).detach()
-            else:
-                w_abs_structured = w_abs.view(filters, -1).sum(dim=1)
-                threshold = torch.topk(w_abs_structured.view(-1), num_prune, largest=False)[0].max()
-                mask_weight = torch.gt(w_abs_structured, threshold)[:, None, None, None].expand_as(weight).type_as(weight)
-
-        return weight.data.mul(mask_weight)
+        wrapper_copy = copy.deepcopy(wrapper)
+        wrapper_copy.module.weight.data = weight
+        return weight.data.mul(self.masker.calc_mask(sparsity, wrapper_copy)['weight_mask'])
 
     def compress(self):
         """
@@ -179,7 +166,7 @@ class ADMMPruner(OneshotPruner):
             # U_i^{k+1} = U^k + W_i^{k+1} - Z_i^{k+1}
             for i, wrapper in enumerate(self.get_modules_wrapper()):
                 z = wrapper.module.weight.data + U[i]
-                Z[i] = self._projection(z, wrapper.config['sparsity'])
+                Z[i] = self._projection(z, wrapper.config['sparsity'], wrapper)
                 U[i] = U[i] + wrapper.module.weight.data - Z[i]
 
         # apply prune

nni/algorithms/compression/pytorch/pruning/auto_compress_pruner.py

@@ -80,7 +80,7 @@ class AutoCompressPruner(Pruner):
     optimize_mode : str
         optimize mode, `maximize` or `minimize`, by default `maximize`.
     base_algo : str
-        Base pruning algorithm. `level`, `l1` or `l2`, by default `l1`. Given the sparsity distribution among the ops,
+        Base pruning algorithm. `level`, `l1`, `l2` or `fpgm`, by default `l1`. Given the sparsity distribution among the ops,
         the assigned `base_algo` is used to decide which filters/channels/weights to prune.
     start_temperature : float
         Start temperature of the simulated annealing process.
@@ -151,7 +151,7 @@ class AutoCompressPruner(Pruner):
                 Optional('op_types'): [str],
                 Optional('op_names'): [str],
             }], model, _logger)
-        elif self._base_algo in ['l1', 'l2']:
+        elif self._base_algo in ['l1', 'l2', 'fpgm']:
             schema = CompressorSchema([{
                 'sparsity': And(float, lambda n: 0 < n < 1),
                 'op_types': ['Conv2d'],

nni/algorithms/compression/pytorch/pruning/constants_pruner.py

@@ -2,10 +2,11 @@
 # Licensed under the MIT license.
 
 
-from .one_shot import LevelPruner, L1FilterPruner, L2FilterPruner
+from .one_shot import LevelPruner, L1FilterPruner, L2FilterPruner, FPGMPruner
 
 PRUNER_DICT = {
     'level': LevelPruner,
     'l1': L1FilterPruner,
-    'l2': L2FilterPruner
+    'l2': L2FilterPruner,
+    'fpgm': FPGMPruner
 }

nni/algorithms/compression/pytorch/pruning/net_adapt_pruner.py

@@ -73,7 +73,7 @@ class NetAdaptPruner(Pruner):
     optimize_mode : str
         optimize mode, `maximize` or `minimize`, by default `maximize`.
     base_algo : str
-        Base pruning algorithm. `level`, `l1` or `l2`, by default `l1`. Given the sparsity distribution among the ops,
+        Base pruning algorithm. `level`, `l1`, `l2` or `fpgm`, by default `l1`. Given the sparsity distribution among the ops,
         the assigned `base_algo` is used to decide which filters/channels/weights to prune.
     sparsity_per_iteration : float
         sparsity to prune in each iteration.
@@ -125,7 +125,7 @@ class NetAdaptPruner(Pruner):
                 Optional('op_types'): [str],
                 Optional('op_names'): [str],
             }], model, _logger)
-        elif self._base_algo in ['l1', 'l2']:
+        elif self._base_algo in ['l1', 'l2', 'fpgm']:
             schema = CompressorSchema([{
                 'sparsity': And(float, lambda n: 0 < n < 1),
                 'op_types': ['Conv2d'],
@@ -149,7 +149,7 @@ class NetAdaptPruner(Pruner):
                 return config_list_updated
 
         # if op_name is not in self._config_list_generated, create a new json item
-        if self._base_algo in ['l1', 'l2']:
+        if self._base_algo in ['l1', 'l2', 'fpgm']:
             config_list_updated.append(
                 {'sparsity': sparsity, 'op_types': ['Conv2d'], 'op_names': [op_name]})
         elif self._base_algo == 'level':

nni/algorithms/compression/pytorch/pruning/sensitivity_pruner.py

@@ -68,7 +68,7 @@ class SensitivityPruner(Pruner):
         >>>             loss.backward()
         >>>             optimizer.step()
     base_algo: str
-        base pruning algorithm. `level`, `l1` or `l2`, by default `l1`.
+        base pruning algorithm. `level`, `l1`, `l2` or `fpgm`, by default `l1`.
     sparsity_proportion_calc: function
         This function generate the sparsity proportion between the conv layers according to the
         sensitivity analysis results. We provide a default function to quantify the sparsity
@@ -150,7 +150,7 @@ class SensitivityPruner(Pruner):
                 Optional('op_types'): [str],
                 Optional('op_names'): [str],
             }], model, _logger)
-        elif self.base_algo in ['l1', 'l2']:
+        elif self.base_algo in ['l1', 'l2', 'fpgm']:
             schema = CompressorSchema([{
                 'sparsity': And(float, lambda n: 0 < n < 1),
                 'op_types': ['Conv2d'],

nni/algorithms/compression/pytorch/pruning/simulated_annealing_pruner.py

@@ -54,7 +54,7 @@ class SimulatedAnnealingPruner(Pruner):
     optimize_mode : str
         Optimize mode, `maximize` or `minimize`, by default `maximize`.
     base_algo : str
-        Base pruning algorithm. `level`, `l1` or `l2`, by default `l1`. Given the sparsity distribution among the ops,
+        Base pruning algorithm. `level`, `l1`, `l2` or `fpgm`, by default `l1`. Given the sparsity distribution among the ops,
         the assigned `base_algo` is used to decide which filters/channels/weights to prune.
     start_temperature : float
         Start temperature of the simulated annealing process.
@@ -120,7 +120,7 @@ class SimulatedAnnealingPruner(Pruner):
                 Optional('op_types'): [str],
                 Optional('op_names'): [str],
             }], model, _logger)
-        elif self._base_algo in ['l1', 'l2']:
+        elif self._base_algo in ['l1', 'l2', 'fpgm']:
             schema = CompressorSchema([{
                 'sparsity': And(float, lambda n: 0 < n < 1),
                 'op_types': ['Conv2d'],
@@ -152,7 +152,7 @@ class SimulatedAnnealingPruner(Pruner):
         # a layer with more weights will have no less pruning rate
         for idx, wrapper in enumerate(self.get_modules_wrapper()):
             # L1Filter Pruner requires to specify op_types
-            if self._base_algo in ['l1', 'l2']:
+            if self._base_algo in ['l1', 'l2', 'fpgm']:
                 config_list.append(
                     {'sparsity': sparsities[idx], 'op_types': ['Conv2d'], 'op_names': [wrapper.name]})
             elif self._base_algo == 'level':

test/ut/sdk/test_pruners.py

@@ -151,12 +151,37 @@ prune_config = {
             lambda model: validate_sparsity(model.conv1, 0.5, model.bias)
         ]
     },
-    'autocompress': {
+    'autocompress_l1': {
         'pruner_class': AutoCompressPruner,
         'config_list': [{
             'sparsity': 0.5,
             'op_types': ['Conv2d'],
         }],
+        'base_algo': 'l1',
+        'trainer': lambda model, optimizer, criterion, epoch, callback : model,
+        'evaluator': lambda model: 0.9,
+        'dummy_input': torch.randn([64, 1, 28, 28]),
+        'validators': []
+    },
+    'autocompress_l2': {
+        'pruner_class': AutoCompressPruner,
+        'config_list': [{
+            'sparsity': 0.5,
+            'op_types': ['Conv2d'],
+        }],
+        'base_algo': 'l2',
+        'trainer': lambda model, optimizer, criterion, epoch, callback : model,
+        'evaluator': lambda model: 0.9,
+        'dummy_input': torch.randn([64, 1, 28, 28]),
+        'validators': []
+    },
+    'autocompress_fpgm': {
+        'pruner_class': AutoCompressPruner,
+        'config_list': [{
+            'sparsity': 0.5,
+            'op_types': ['Conv2d'],
+        }],
+        'base_algo': 'fpgm',
         'trainer': lambda model, optimizer, criterion, epoch, callback : model,
         'evaluator': lambda model: 0.9,
         'dummy_input': torch.randn([64, 1, 28, 28]),
@@ -181,7 +206,7 @@ class Model(nn.Module):
     def forward(self, x):
         return self.fc(self.pool(self.bn1(self.conv1(x))).view(x.size(0), -1))
 
-def pruners_test(pruner_names=['level', 'agp', 'slim', 'fpgm', 'l1', 'l2', 'taylorfo', 'mean_activation', 'apoz', 'netadapt', 'simulatedannealing', 'admm', 'autocompress'], bias=True):
+def pruners_test(pruner_names=['level', 'agp', 'slim', 'fpgm', 'l1', 'l2', 'taylorfo', 'mean_activation', 'apoz', 'netadapt', 'simulatedannealing', 'admm', 'autocompress_l1', 'autocompress_l2', 'autocompress_fpgm',], bias=True):
     for pruner_name in pruner_names:
         print('testing {}...'.format(pruner_name))
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -203,8 +228,8 @@ def pruners_test(pruner_names=['level', 'agp', 'slim', 'fpgm', 'l1', 'l2', 'tayl
             pruner = prune_config[pruner_name]['pruner_class'](model, config_list, evaluator=prune_config[pruner_name]['evaluator'])
         elif pruner_name == 'admm':
             pruner = prune_config[pruner_name]['pruner_class'](model, config_list, trainer=prune_config[pruner_name]['trainer'])
-        elif pruner_name == 'autocompress':
-            pruner = prune_config[pruner_name]['pruner_class'](model, config_list, trainer=prune_config[pruner_name]['trainer'], evaluator=prune_config[pruner_name]['evaluator'], dummy_input=x)
+        elif pruner_name.startswith('autocompress'):
+            pruner = prune_config[pruner_name]['pruner_class'](model, config_list, trainer=prune_config[pruner_name]['trainer'], evaluator=prune_config[pruner_name]['evaluator'], dummy_input=x, base_algo=prune_config[pruner_name]['base_algo'])
         else:
             pruner = prune_config[pruner_name]['pruner_class'](model, config_list, optimizer)
         pruner.compress()
@@ -272,7 +297,7 @@ class PrunerTestCase(TestCase):
         pruners_test(bias=False)
 
     def test_agp_pruner(self):
-        for pruning_algorithm in ['l1', 'l2', 'taylorfo', 'apoz']:
+        for pruning_algorithm in ['l1', 'l2', 'fpgm', 'taylorfo', 'apoz']:
             _test_agp(pruning_algorithm)
 
         for pruning_algorithm in ['level']: