remove unused

Signed-off-by: Evelina Bakhturina <ebakhturina@nvidia.com>

examples/nlp/dialogue_state_tracking/rule_based_policy_multiwoz.py

@@ -128,17 +128,18 @@ def init_session():
     return '', '', default_state()
 
 
-def get_system_responce(user_uttr, system_uttr, dialog_history, state):
+def get_system_response(user_uttr, system_uttr, dialog_history, state):
     """
-    Returns system reply by passing user utterance through TRADE Dialogue State Tracker, then the output of the TRADE model to the
-    Rule-base Dialogue Policy Magager and the output of the Policy Manager to the Rule-based Natural language generation module
+    Returns system reply by passing system and user utterances (dialogue history) through the TRADE Dialogue State Tracker,
+        then the output of the TRADE model goes to the Rule-base Dialogue Policy Magager
+        and the output of the Policy Manager goes to the Rule-based Natural language generation module
     Args:
-        user_uttr(str): User utterance
-        system_uttr(str): Previous system utterance
-        dialog_history(str): Diaglogue history contains all previous system and user utterances
+        user_uttr (str): User utterance
+        system_uttr (str): Previous system utterance
+        dialog_history (str): Diaglogue history contains all previous system and user utterances
         state (dict): dialogue state
     Returns:
-        system_utterance(str): system response
+        system_utterance (str): system response
         state (dict): updated dialogue state 
     """
     src_ids, src_lens = utterance_encoder.forward(state=state, user_uttr=user_uttr, sys_uttr=system_uttr)
@@ -177,12 +178,12 @@ def get_system_responce(user_uttr, system_uttr, dialog_history, state):
             system_uttr, dialog_history, state = init_session()
             logging.info("============ Starting a new dialogue ============")
         else:
-            get_system_responce(user_uttr, system_uttr, dialog_history, state)
+            get_system_response(user_uttr, system_uttr, dialog_history, state)
 
 elif args.mode == 'example':
     for example in examples:
         logging.info("============ Starting a new dialogue ============")
         system_uttr, dialog_history, state = init_session()
         for user_uttr in example:
             logging.info("User utterance: %s", user_uttr)
-            system_uttr, state = get_system_responce(user_uttr, system_uttr, dialog_history, state)
+            system_uttr, state = get_system_response(user_uttr, system_uttr, dialog_history, state)

nemo/collections/nlp/nm/non_trainables/dialogue_state_tracking/rule_based_multiwoz_bot.py

@@ -203,8 +203,6 @@ def forward(self, state):
                 for user_act_ in user_acts:
                     del DA[user_act_]
 
-        # print("Sys action: ", DA)
-
         if DA == {}:
             DA = {'general-greet': [['none', 'none']]}
         tuples = []
@@ -284,12 +282,6 @@ def _update_DA(self, user_act, user_action, state, DA):
         kb_result = self.db.query(domain.lower(), constraints)
         self.kb_result[domain] = deepcopy(kb_result)
 
-        # print("\tConstraint: " + "{}".format(constraints))
-        # print("\tCandidate Count: " + "{}".format(len(kb_result)))
-        # if len(kb_result) > 0:
-        #     print("Candidate: " + "{}".format(kb_result[0]))
-
-        # print(state['user_action'])
         # Respond to user's request
         if intent_type == 'Request':
             if self.recommend_flag > 1:
@@ -310,7 +302,6 @@ def _update_DA(self, user_act, user_action, state, DA):
 
         else:
             # There's no result matching user's constraint
-            # if len(state['kb_results_dict']) == 0:
             if len(kb_result) == 0:
                 if (domain + "-NoOffer") not in DA:
                     DA[domain + "-NoOffer"] = []
@@ -468,8 +459,6 @@ def _update_train(self, user_act, user_action, state, DA):
         kb_result = self.db.query('train', constraints)
         self.kb_result['Train'] = deepcopy(kb_result)
 
-        # print(constraints)
-        # print(len(kb_result))
         if user_act == 'Train-Request':
             del DA['Train-Request']
             if 'Train-Inform' not in DA:

nemo/collections/nlp/nm/non_trainables/dialogue_state_tracking/trade_state_update_nm.py

@@ -16,7 +16,8 @@
 
 '''
 This file contains code artifacts adapted from the original implementation:
-https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/policy/rule/multiwoz/rule_based_multiwoz_bot.py
+https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/dst/trade/multiwoz/trade.py
+https://github.com/thu-coai/ConvLab-2
 '''
 import copy
 import re
@@ -33,76 +34,6 @@
 __all__ = ['TradeStateUpdateNM']
 
 
-# class UtteranceEncoderNM(NonTrainableNM):
-#     """
-#     Encodes dialogue history (system and user utterances) into a Multiwoz dataset format
-#     Args:
-#         data_desc (obj): data descriptor for MultiWOZ dataset, contains information about domains, slots,
-#             and associated vocabulary
-#     """
-
-#     @property
-#     @add_port_docs()
-#     def input_ports(self):
-#         """Returns definitions of module input ports.
-#         state (dict): dialogue state dictionary - see nemo.collections.nlp.data.datasets.multiwoz_dataset.state
-#             for the format
-#         user_uttr (str): user utterance
-#         sys_uttr (str): system utterace
-#         """
-#         return {
-#             "state": NeuralType(axes=tuple('ANY'), element_type=VoidType()),
-#             "user_uttr": NeuralType(axes=tuple('ANY'), element_type=VoidType()),
-#             "sys_uttr": NeuralType(axes=tuple('ANY'), element_type=VoidType())
-#         }
-
-
-#     @property
-#     @add_port_docs()
-#     def output_ports(self):
-#         """Returns definitions of module output ports.
-#         src_ids (int): token ids for dialogue history
-#         src_lens (int): length of the tokenized dialogue history
-#         """
-#         return {
-#             'src_ids': NeuralType(('B', 'T'), element_type=ChannelType()),
-#             'src_lens': NeuralType(tuple('B'), elemenet_type=LengthsType()),
-#         }
-
-#     def __init__(self, data_desc):
-#         """
-#         Init
-#         Args:
-#             data_desc (obj): data descriptor for MultiWOZ dataset, contains information about domains, slots,
-#                     and associated vocabulary
-#         """
-#         super().__init__()
-#         self.data_desc = data_desc
-
-#     def forward(self, state, user_uttr, sys_uttr):
-#         """
-#         Returns dialogue utterances in the format accepted by the TRADE Dialogue state tracking model
-#         Args:
-#             state (dict): state dictionary - see nemo.collections.nlp.data.datasets.multiwoz_dataset.state
-#                 for the format
-#             user_uttr (str): user utterance
-#             sys_uttr (str): system utterace
-#         Returns:
-#             src_ids (int): token ids for dialogue history
-#             src_lens (int): length of the tokenized dialogue history
-#         """
-#         state["history"].append(["sys", sys_uttr])
-#         state["history"].append(["user", user_uttr])
-#         state["user_action"] = user_uttr
-#         logging.debug("Dialogue state: %s", state)
-
-#         context = ' ; '.join([item[1].strip().lower() for item in state['history']]).strip() + ' ;'
-#         context_ids = self.data_desc.vocab.tokens2ids(context.split())
-#         src_ids = torch.tensor(context_ids).unsqueeze(0).to(self._device)
-#         src_lens = torch.tensor(len(context_ids)).unsqueeze(0).to(self._device)
-#         return src_ids, src_lens
-
-
 class TradeStateUpdateNM(NonTrainableNM):
     """
      Takes the predictions of the TRADE Dialogue state tracking model, 
@@ -288,9 +219,7 @@ def normalize_value(self, value_set, domain, slot, value):
         if slot not in value_set[domain]:
             logging.warning('slot {} no in domain {}'.format(slot, domain))
             return value
-            # raise Exception(
-            #     'slot <{}> not found in db_values[{}]'.format(
-            #         slot, domain))
+
         value_list = value_set[domain][slot]
         # exact match or containing match
         v = self._match_or_contain(value, value_list)