fightihg with belief state

Signed-off-by: nvidia <[email protected]>

examples/nlp/dialogue_state_tracking/rule_based_policy_multiwoz.py

@@ -101,7 +101,7 @@ def forward(dialog_pipeline, system_uttr, user_uttr, dial_history, belief_state)
     )
 
     # 2. Forward pass throught Dialog Policy Manager module (Rule-Based, queries a "simple DB" to get required data).
-    system_acts, belief_state = dialog_pipeline.modules[dialog_pipeline.steps[4]].forward(
+    belief_state, system_acts = dialog_pipeline.modules[dialog_pipeline.steps[4]].forward(
         belief_state=belief_state, request_state=request_state
     )
 

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

@@ -68,23 +68,7 @@ class TemplateNLGMultiWOZNM(NonTrainableNM):
     """Generate a natural language utterance conditioned on the dialog act.
     """
 
-    @property
-    @add_port_docs()
-    def input_ports(self):
-        """Returns definitions of module input ports.
-        system_acts (list): list of system actions action produced by dialog policy module
-        """
-        return {"system_acts": NeuralType(axes=tuple('ANY'), elements_type=VoidType())}
-
-    @property
-    @add_port_docs()
-    def output_ports(self):
-        """Returns definitions of module output ports.
-        system_uttr (str): generated system's response
-        """
-        return {"system_uttr": NeuralType(axes=(AxisType(kind=AxisKind.Time)), elements_type=StringType())}
-
-    def __init__(self, mode="auto_manual"):
+    def __init__(self, mode="auto_manual", name=None):
         """
         Initializes the object
         Args:
@@ -94,6 +78,9 @@ def __init__(self, mode="auto_manual"):
                 - `auto_manual`: use auto templates first. When fails, use manual templates.
                 both template are dict, *_template[dialog_act][slot] is a list of templates.
         """
+        # Call base class constructor.
+        NonTrainableNM.__init__(self, name=name)
+
         self.mode = mode
         template_dir = os.path.dirname(os.path.abspath(__file__))
 
@@ -104,6 +91,22 @@ def read_json(filename):
         self.auto_system_template = read_json(os.path.join(template_dir, 'auto_system_template_nlg.json'))
         self.manual_system_template = read_json(os.path.join(template_dir, 'manual_system_template_nlg.json'))
 
+    @property
+    @add_port_docs()
+    def input_ports(self):
+        """Returns definitions of module input ports.
+        system_acts (list): list of system actions action produced by dialog policy module
+        """
+        return {"system_acts": NeuralType(axes=tuple('ANY'), elements_type=VoidType())}
+
+    @property
+    @add_port_docs()
+    def output_ports(self):
+        """Returns definitions of module output ports.
+        system_uttr (str): generated system's response
+        """
+        return {"system_uttr": NeuralType(axes=(AxisType(kind=AxisKind.Time)), elements_type=StringType())}
+
     def forward(self, system_acts):
         """
         Generates system response 

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

@@ -32,7 +32,7 @@
 from nemo.backends.pytorch.nm import NonTrainableNM
 from nemo.collections.nlp.data.datasets.multiwoz_dataset.dbquery import Database
 from nemo.collections.nlp.data.datasets.multiwoz_dataset.multiwoz_slot_trans import REF_SYS_DA, REF_USR_DA
-from nemo.core import AxisKind, AxisType, NeuralType, StringType, VoidType
+from nemo.core.neural_types import *
 from nemo.utils.decorators import add_port_docs
 
 __all__ = ['RuleBasedMultiwozBotNM']
@@ -93,14 +93,42 @@ class RuleBasedMultiwozBotNM(NonTrainableNM):
      Rule-based bot. Implemented for Multiwoz dataset.
     """
 
+    def __init__(self, data_dir: str, name: str = None):
+        """
+        Initializes the object
+        Args:
+            data_dir (str): path to data directory
+            name: name of the modules (DEFAULT: none)
+        """
+        # Call base class constructor.
+        NonTrainableNM.__init__(self, name=name)
+        # Set init values of attributes.
+        self.last_state = {}
+        self.db = Database(data_dir)
+        self.last_request_state = {}
+        self.last_belief_state = {}
+        self.recommend_flag = -1
+        self.choice = ""
+
     @property
     @add_port_docs()
     def input_ports(self):
         """Returns definitions of module input ports.
         """
         return {
-            'belief_state': NeuralType(axes=(AxisType(kind=AxisKind.Time, is_list=True)), elements_type=StringType()),
-            'request_state': NeuralType(axes=(AxisType(kind=AxisKind.Time)), elements_type=StringType()),
+            'belief_state': NeuralType(
+                axes=[
+                    AxisType(kind=AxisKind.Batch, is_list=True),
+                    AxisType(
+                        kind=AxisKind.MultiWOZDomain, is_list=True
+                    ),  # always 7 domains - but cannot set size with is_list!
+                ],
+                elements_type=Length(),
+            ),
+            'request_state': NeuralType(
+                axes=[AxisType(kind=AxisKind.Batch, is_list=True), AxisType(kind=AxisKind.Sequence, is_list=True)],
+                elements_type=StringType(),
+            ),
         }
 
     @property
@@ -111,32 +139,30 @@ def output_ports(self):
         belief_state (dict): dialogue state with slot-slot_values pairs for all domains
         """
         return {
-            'system_acts': NeuralType(axes=tuple('ANY'), elements_type=VoidType()),
-            'belief_state': NeuralType(axes=(AxisType(kind=AxisKind.Time, is_list=True)), elements_type=StringType()),
+            'belief_state': NeuralType(
+                axes=[
+                    AxisType(kind=AxisKind.Batch, is_list=True),
+                    AxisType(
+                        kind=AxisKind.MultiWOZDomain, is_list=True
+                    ),  # always 7 domains - but cannot set size with is_list!
+                ],
+                elements_type=Length(),
+            ),
+            'system_acts': NeuralType(
+                axes=[AxisType(kind=AxisKind.Batch, is_list=True), AxisType(kind=AxisKind.Sequence, is_list=True)],
+                elements_type=StringType(),
+            ),
         }
 
-    def __init__(self, data_dir):
-        """
-        Initializes the object
-        Args:
-            data_dir (str): path to data directory
-        """
-        self.last_state = {}
-        self.db = Database(data_dir)
-        self.last_request_state = {}
-        self.last_belief_state = {}
-        self.recommend_flag = -1
-        self.choice = ""
-
     def forward(self, belief_state, request_state):
         """
         Generated System Act and add it to the belief state
         Args:
             belief_state (dict): dialogue state with slot-slot_values pairs for all domains
             request_state (dict): requested slots dict
         Returns:
-            system_acts (list): DA(Dialog Act), in the form of {act_type1: [[slot_name_1, value_1], [slot_name_2, value_2], ...], ...}
             belief_state (dict): updated belief state
+            system_acts (list): DA(Dialog Act), in the form of {act_type1: [[slot_name_1, value_1], [slot_name_2, value_2], ...], ...}
         """
 
         if self.recommend_flag != -1:
@@ -198,7 +224,7 @@ def forward(self, belief_state, request_state):
         logging.debug("DPM output: %s", system_acts)
         logging.debug("Belief State after DPM: %s", belief_state)
         logging.debug("Request State after DPM: %s", request_state)
-        return system_acts, belief_state
+        return belief_state, system_acts
 
     def _update_greeting(self, user_act, DA):
         """ General request / inform. """

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

@@ -54,7 +54,7 @@ def input_ports(self):
                     AxisType(kind=AxisKind.Batch, is_list=True),
                     AxisType(kind=AxisKind.MultiWOZDomain, is_list=True),  # 7 domains
                 ],
-                elements_type=MultiWOZSlotValue(),
+                elements_type=Length(),
             ),
             'user_uttr': NeuralType(axes=[AxisType(kind=AxisKind.Batch, is_list=True)], elements_type=Utterance()),
         }
@@ -66,11 +66,14 @@ def output_ports(self):
         """
         return {
             'belief_state': NeuralType(
-                axes=[AxisType(kind=AxisKind.Batch, is_list=True), AxisType(kind=AxisKind.Time, is_list=True)],
-                elements_type=StringType(),
+                axes=[
+                    AxisType(kind=AxisKind.Batch, is_list=True),
+                    AxisType(kind=AxisKind.MultiWOZDomain, is_list=True),  # 7 domains
+                ],
+                elements_type=Length(),
             ),
             'request_state': NeuralType(
-                axes=[AxisType(kind=AxisKind.Batch, is_list=True), AxisType(kind=AxisKind.Time, is_list=True)],
+                axes=[AxisType(kind=AxisKind.Batch, is_list=True), AxisType(kind=AxisKind.Sequence, is_list=True)],
                 elements_type=StringType(),
             ),
         }

nemo/core/neural_types/elements.py

@@ -280,10 +280,6 @@ class Length(IntType):
     """Type representing an element storing a "length" (e.g. length of a list)."""
 
 
-class Length(IntType):
-    """Type representing an element storing a "length" (e.g. length of a list)."""
-
-
 class SlotValue(ElementType):
     """Element type representing slot-value pair."""
 

nemo/core/neural_types/neural_type.py

@@ -121,7 +121,7 @@ def compare_and_raise_error(self, parent_type_name, port_name, second_object):
             and type_comatibility != NeuralTypeComparisonResult.GREATER
         ):
             raise NeuralPortNmTensorMismatchError(
-                parent_type_name, port_name, str(self), str(second_object), type_comatibility
+                parent_type_name, port_name, str(self), str(second_object.ntype), type_comatibility
             )
 
     @staticmethod