fix total sparsity in sa & auto compress pruner bug (#4474)

examples/model_compress/pruning/v2/simulated_anealing_pruning_torch.py

@@ -99,7 +99,7 @@ if __name__ == '__main__':
         trainer(model, optimizer, criterion, i)
         evaluator(model)
 
-    config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
+    config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.8}]
 
     # evaluator in 'SimulatedAnnealingPruner' could not be None.
     pruner = SimulatedAnnealingPruner(model, config_list, pruning_algorithm=args.pruning_algo,

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

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+import logging
 from pathlib import Path
 from typing import Dict, List, Callable, Optional
 
@@ -13,6 +14,8 @@ from .basic_pruner import ADMMPruner
 from .iterative_pruner import IterativePruner, SimulatedAnnealingPruner
 from .tools import LotteryTicketTaskGenerator
 
+_logger = logging.getLogger(__name__)
+
 
 class AutoCompressTaskGenerator(LotteryTicketTaskGenerator):
     def __init__(self, total_iteration: int, origin_model: Module, origin_config_list: List[Dict],
@@ -29,6 +32,13 @@ class AutoCompressTaskGenerator(LotteryTicketTaskGenerator):
                          log_dir=log_dir,
                          keep_intermediate_result=keep_intermediate_result)
 
+    def reset(self, model: Module, config_list: List[Dict] = [], masks: Dict[str, Dict[str, Tensor]] = {}):
+        # TODO: replace with validation here
+        for config in config_list:
+            if 'sparsity' in config or 'sparsity_per_layer' in config:
+                _logger.warning('Only `total_sparsity` can be differentially allocated sparse ratio to each layer, `sparsity` or `sparsity_per_layer` will allocate fixed sparse ratio to layers. Make sure you know what this will lead to, otherwise please use `total_sparsity`.')
+        return super().reset(model, config_list, masks)
+
     def _iterative_pruner_reset(self, model: Module, config_list: List[Dict] = [], masks: Dict[str, Dict[str, Tensor]] = {}):
         self.iterative_pruner.task_generator._log_dir = Path(self._log_dir_root, 'SA')
         self.iterative_pruner.reset(model, config_list=config_list, masks=masks)

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

@@ -187,6 +187,11 @@ class SimulatedAnnealingTaskGenerator(TaskGenerator):
     def reset(self, model: Module, config_list: List[Dict] = [], masks: Dict[str, Dict[str, Tensor]] = {}):
         self.current_temperature = self.start_temperature
 
+        # TODO: replace with validation here
+        for config in config_list:
+            if 'sparsity' in config or 'sparsity_per_layer' in config:
+                _logger.warning('Only `total_sparsity` can be differentially allocated sparse ratio to each layer, `sparsity` or `sparsity_per_layer` will allocate fixed sparse ratio to layers. Make sure you know what this will lead to, otherwise please use `total_sparsity`.')
+
         self.weights_numel, self.masked_rate = get_model_weights_numel(model, config_list, masks)
         self.target_sparsity_list = config_list_canonical(model, config_list)
         self._adjust_target_sparsity()
@@ -281,7 +286,10 @@ class SimulatedAnnealingTaskGenerator(TaskGenerator):
         magnitude = self.current_temperature / self.start_temperature * self.perturbation_magnitude
         for config, current_sparsity in zip(self.target_sparsity_list, self._current_sparsity_list):
             if len(current_sparsity) == 0:
-                self._temp_config_list.extend(deepcopy(config))
+                sub_temp_config_list = [deepcopy(config) for i in range(len(config['op_names']))]
+                for temp_config, op_name in zip(sub_temp_config_list, config['op_names']):
+                    temp_config.update({'total_sparsity': 0, 'op_names': [op_name]})
+                self._temp_config_list.extend(sub_temp_config_list)
                 self._temp_sparsity_list.append([])
                 continue
             while True:

test/ut/compression/v2/test_iterative_pruner_torch.py

@@ -98,7 +98,7 @@ class IterativePrunerTestCase(unittest.TestCase):
 
     def test_simulated_annealing_pruner(self):
         model = TorchModel()
-        config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
+        config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.8}]
         pruner = SimulatedAnnealingPruner(model, config_list, evaluator, start_temperature=40, log_dir='../../../logs')
         pruner.compress()
         _, pruned_model, masks, _, _ = pruner.get_best_result()
@@ -107,7 +107,7 @@ class IterativePrunerTestCase(unittest.TestCase):
 
     def test_auto_compress_pruner(self):
         model = TorchModel()
-        config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
+        config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.8}]
         admm_params = {
             'trainer': trainer,
             'traced_optimizer': get_optimizer(model),