Compressor updates

examples/model_compress/model_prune_torch.py

@@ -142,10 +142,10 @@ def create_model(model_name='naive'):
     else:
         return VGG(19)
 
-def create_pruner(model, pruner_name):
+def create_pruner(model, pruner_name, optimizer=None):
     pruner_class = prune_config[pruner_name]['pruner_class']
     config_list = prune_config[pruner_name]['config_list']
-    return pruner_class(model, config_list)
+    return pruner_class(model, config_list, optimizer)
 
 def train(model, device, train_loader, optimizer):
     model.train()
@@ -179,6 +179,8 @@ def test(model, device, test_loader):
 
 def main(args):
     device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+    if not os.path.exists(args.checkpoints_dir):
+        os.makedirs(args.checkpoints_dir)
 
     model_name = prune_config[args.pruner_name]['model_name']
     dataset_name = prune_config[args.pruner_name]['dataset_name']
@@ -203,8 +205,6 @@ def main(args):
 
     print('start model pruning...')
 
-    if not os.path.exists(args.checkpoints_dir):
-        os.makedirs(args.checkpoints_dir)
     model_path = os.path.join(args.checkpoints_dir, 'pruned_{}_{}_{}.pth'.format(model_name, dataset_name, args.pruner_name))
     mask_path = os.path.join(args.checkpoints_dir, 'mask_{}_{}_{}.pth'.format(model_name, dataset_name, args.pruner_name))
 

examples/trials/mnist-pytorch/mnist.py

@@ -15,6 +15,13 @@
 import torch.optim as optim
 from torchvision import datasets, transforms
 
+# Temporary patch this example until the MNIST dataset download issue get resolved
+# https://github.com/pytorch/vision/issues/1938
+import urllib
+
+opener = urllib.request.build_opener()
+opener.addheaders = [('User-agent', 'Mozilla/5.0')]
+urllib.request.install_opener(opener)
 
 logger = logging.getLogger('mnist_AutoML')
 

src/sdk/pynni/nni/compression/torch/activation_rank_filter_pruners.py

@@ -16,7 +16,7 @@ class ActivationRankFilterPruner(Pruner):
     to achieve a preset level of network sparsity.
     """
 
-    def __init__(self, model, config_list, optimizer, activation='relu', statistics_batch_num=1):
+    def __init__(self, model, config_list, optimizer=None, activation='relu', statistics_batch_num=1):
         """
         Parameters
         ----------
@@ -25,6 +25,8 @@ def __init__(self, model, config_list, optimizer, activation='relu', statistics_
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         activation : str
             Activation function
         statistics_batch_num : int
@@ -105,7 +107,7 @@ class ActivationAPoZRankFilterPruner(ActivationRankFilterPruner):
     https://arxiv.org/abs/1607.03250
     """
 
-    def __init__(self, model, config_list, optimizer, activation='relu', statistics_batch_num=1):
+    def __init__(self, model, config_list, optimizer=None, activation='relu', statistics_batch_num=1):
         """
         Parameters
         ----------
@@ -114,6 +116,8 @@ def __init__(self, model, config_list, optimizer, activation='relu', statistics_
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         activation : str
             Activation function
         statistics_batch_num : int
@@ -177,7 +181,7 @@ class ActivationMeanRankFilterPruner(ActivationRankFilterPruner):
     https://arxiv.org/abs/1611.06440
     """
 
-    def __init__(self, model, config_list, optimizer, activation='relu', statistics_batch_num=1):
+    def __init__(self, model, config_list, optimizer=None, activation='relu', statistics_batch_num=1):
         """
         Parameters
         ----------
@@ -186,6 +190,8 @@ def __init__(self, model, config_list, optimizer, activation='relu', statistics_
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         activation : str
             Activation function
         statistics_batch_num : int

src/sdk/pynni/nni/compression/torch/compressor.py

@@ -27,7 +27,7 @@ class Compressor:
     Abstract base PyTorch compressor
     """
 
-    def __init__(self, model, config_list, optimizer):
+    def __init__(self, model, config_list, optimizer=None):
         """
         Record necessary info in class members
 
@@ -235,7 +235,8 @@ def new_step(_, *args, **kwargs):
                     task()
                 return output
             return new_step
-        self.optimizer.step = types.MethodType(patch_step(self.optimizer.step), self.optimizer)
+        if self.optimizer is not None:
+            self.optimizer.step = types.MethodType(patch_step(self.optimizer.step), self.optimizer)
 
 class PrunerModuleWrapper(torch.nn.Module):
     def __init__(self, module, module_name, module_type, config, pruner):
@@ -290,9 +291,10 @@ class Pruner(Compressor):
 
     """
 
-    def __init__(self, model, config_list, optimizer):
+    def __init__(self, model, config_list, optimizer=None):
         super().__init__(model, config_list, optimizer)
-        self.patch_optimizer(self.update_mask)
+        if optimizer is not None:
+            self.patch_optimizer(self.update_mask)
 
     def compress(self):
         self.update_mask()

src/sdk/pynni/nni/compression/torch/pruners.py

@@ -16,14 +16,16 @@ class LevelPruner(Pruner):
     Prune to an exact pruning level specification
     """
 
-    def __init__(self, model, config_list, optimizer):
+    def __init__(self, model, config_list, optimizer=None):
         """
         Parameters
         ----------
         model : torch.nn.module
             Model to be pruned
         config_list : list
             List on pruning configs
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -78,9 +80,13 @@ def __init__(self, model, config_list, optimizer):
             Model to be pruned
         config_list : list
             List on pruning configs
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
+        assert isinstance(optimizer, torch.optim.Optimizer), "AGP pruner is an iterative pruner, please pass optimizer of the model to it"
+
         self.now_epoch = 0
         self.set_wrappers_attribute("if_calculated", False)
 
@@ -176,13 +182,17 @@ class SlimPruner(Pruner):
     https://arxiv.org/pdf/1708.06519.pdf
     """
 
-    def __init__(self, model, config_list, optimizer):
+    def __init__(self, model, config_list, optimizer=None):
         """
         Parameters
         ----------
+        model : torch.nn.module
+            Model to be pruned
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -244,7 +254,7 @@ class LotteryTicketPruner(Pruner):
     5. Repeat step 2, 3, and 4.
     """
 
-    def __init__(self, model, config_list, optimizer, lr_scheduler=None, reset_weights=True):
+    def __init__(self, model, config_list, optimizer=None, lr_scheduler=None, reset_weights=True):
         """
         Parameters
         ----------

src/sdk/pynni/nni/compression/torch/weight_rank_filter_pruners.py

@@ -15,7 +15,7 @@ class WeightRankFilterPruner(Pruner):
     importance criterion in convolution layers to achieve a preset level of network sparsity.
     """
 
-    def __init__(self, model, config_list, optimizer):
+    def __init__(self, model, config_list, optimizer=None):
         """
         Parameters
         ----------
@@ -24,6 +24,8 @@ def __init__(self, model, config_list, optimizer):
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -83,7 +85,7 @@ class L1FilterPruner(WeightRankFilterPruner):
     https://arxiv.org/abs/1608.08710
     """
 
-    def __init__(self, model, config_list, optimizer):
+    def __init__(self, model, config_list, optimizer=None):
         """
         Parameters
         ----------
@@ -92,6 +94,8 @@ def __init__(self, model, config_list, optimizer):
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -131,7 +135,7 @@ class L2FilterPruner(WeightRankFilterPruner):
     smallest L2 norm of the weights.
     """
 
-    def __init__(self, model, config_list, optimizer):
+    def __init__(self, model, config_list, optimizer=None):
         """
         Parameters
         ----------
@@ -140,6 +144,8 @@ def __init__(self, model, config_list, optimizer):
         config_list : list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         """
 
         super().__init__(model, config_list, optimizer)
@@ -187,8 +193,11 @@ def __init__(self, model, config_list, optimizer):
         config_list: list
             support key for each list item:
                 - sparsity: percentage of convolutional filters to be pruned.
+        optimizer: torch.optim.Optimizer
+            Optimizer used to train model
         """
         super().__init__(model, config_list, optimizer)
+        assert isinstance(optimizer, torch.optim.Optimizer), "FPGM pruner is an iterative pruner, please pass optimizer of the model to it"
 
     def get_mask(self, base_mask, weight, num_prune):
         """

src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py

@@ -4,6 +4,7 @@
 import logging
 import datetime
 from nni.assessor import Assessor, AssessResult
+from nni.utils import extract_scalar_history
 from .model_factory import CurveModel
 
 logger = logging.getLogger('curvefitting_Assessor')
@@ -91,10 +92,11 @@ def assess_trial(self, trial_job_id, trial_history):
         Exception
             unrecognize exception in curvefitting_assessor
         """
-        self.trial_history = trial_history
+        scalar_trial_history = extract_scalar_history(trial_history)
+        self.trial_history = scalar_trial_history
         if not self.set_best_performance:
             return AssessResult.Good
-        curr_step = len(trial_history)
+        curr_step = len(scalar_trial_history)
         if curr_step < self.start_step:
             return AssessResult.Good
 
@@ -106,7 +108,7 @@ def assess_trial(self, trial_job_id, trial_history):
             start_time = datetime.datetime.now()
             # Predict the final result
             curvemodel = CurveModel(self.target_pos)
-            predict_y = curvemodel.predict(trial_history)
+            predict_y = curvemodel.predict(scalar_trial_history)
             logger.info('Prediction done. Trial job id = %s. Predict value = %s', trial_job_id, predict_y)
             if predict_y is None:
                 logger.info('wait for more information to predict precisely')

src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py

@@ -3,6 +3,7 @@
 
 import logging
 from nni.assessor import Assessor, AssessResult
+from nni.utils import extract_scalar_history
 
 logger = logging.getLogger('medianstop_Assessor')
 
@@ -91,20 +92,12 @@ def assess_trial(self, trial_job_id, trial_history):
         if curr_step < self._start_step:
             return AssessResult.Good
 
-        try:
-            num_trial_history = [float(ele) for ele in trial_history]
-        except (TypeError, ValueError) as error:
-            logger.warning('incorrect data type or value:')
-            logger.exception(error)
-        except Exception as error:
-            logger.warning('unrecognized exception in medianstop_assessor:')
-            logger.exception(error)
-
-        self._update_data(trial_job_id, num_trial_history)
+        scalar_trial_history = extract_scalar_history(trial_history)
+        self._update_data(trial_job_id, scalar_trial_history)
         if self._high_better:
-            best_history = max(trial_history)
+            best_history = max(scalar_trial_history)
         else:
-            best_history = min(trial_history)
+            best_history = min(scalar_trial_history)
 
         avg_array = []
         for id_ in self._completed_avg_history:

src/sdk/pynni/nni/msg_dispatcher.py

@@ -234,4 +234,5 @@ def _earlystop_notify_tuner(self, data):
         if multi_thread_enabled():
             self._handle_final_metric_data(data)
         else:
+            data['value'] = to_json(data['value'])
             self.enqueue_command(CommandType.ReportMetricData, data)

src/sdk/pynni/nni/utils.py

@@ -62,6 +62,13 @@ def extract_scalar_reward(value, scalar_key='default'):
     """
     Extract scalar reward from trial result.
 
+    Parameters
+    ----------
+    value : int, float, dict
+        the reported final metric data
+    scalar_key : str
+        the key name that indicates the numeric number
+
     Raises
     ------
     RuntimeError
@@ -78,6 +85,26 @@ def extract_scalar_reward(value, scalar_key='default'):
     return reward
 
 
+def extract_scalar_history(trial_history, scalar_key='default'):
+    """
+    Extract scalar value from a list of intermediate results.
+
+    Parameters
+    ----------
+    trial_history : list
+        accumulated intermediate results of a trial
+    scalar_key : str
+        the key name that indicates the numeric number
+
+    Raises
+    ------
+    RuntimeError
+        Incorrect final result: the final result should be float/int,
+        or a dict which has a key named "default" whose value is float/int.
+    """
+    return [extract_scalar_reward(ele, scalar_key) for ele in trial_history]
+
+
 def convert_dict2tuple(value):
     """
     convert dict type to tuple to solve unhashable problem.
@@ -90,7 +117,9 @@ def convert_dict2tuple(value):
 
 
 def init_dispatcher_logger():
-    """ Initialize dispatcher logging configuration"""
+    """
+    Initialize dispatcher logging configuration
+    """
     logger_file_path = 'dispatcher.log'
     if dispatcher_env_vars.NNI_LOG_DIRECTORY is not None:
         logger_file_path = os.path.join(dispatcher_env_vars.NNI_LOG_DIRECTORY, logger_file_path)