first version where SGD data set is running, now have to test whether it learns properly

convlab/policy/evaluate.py

@@ -58,7 +58,7 @@ def evaluate(config_path, model_name, verbose=False, model_path="", goals_from_d
         from convlab.policy.gdpl import GDPL
         policy_sys = GDPL(vectorizer=conf['vectorizer_sys_activated'])
     elif model_name == "DDPT":
-        from convlab2.policy.vtrace_DPT import VTRACE
+        from convlab.policy.vtrace_DPT import VTRACE
         policy_sys = VTRACE(is_train=False, vectorizer=conf['vectorizer_sys_activated'])
 
     try:

convlab/policy/vector/vector_nodes.py

@@ -100,6 +100,8 @@ class VectorNodes(VectorBase):
 
         feature_type = 'user goal'
         for domain in self.belief_domains:
+            # the if case is needed because SGD only saves the dialogue state info for active domains
+            if domain in state['belief_state']:
                 for slot, value in state['belief_state'][domain].items():
                     description = f"user goal-{domain}-{slot}".lower()
                     value = 1.0 if (value and value != "not mentioned") else 0.0

convlab/policy/vtrace_DPT/create_descriptions.py

@@ -11,6 +11,7 @@ def create_description_dicts(name='multiwoz21'):
     ontology = load_ontology(name)
     default_state = ontology['state']
     domains = list(ontology['domains'].keys())
+
     try:
         db = load_database(name)
         db_domains = db.domains
@@ -31,22 +32,27 @@ def create_description_dicts(name='multiwoz21'):
 
     for domain in default_state:
         for slot in default_state[domain]:
+            domain = domain.lower()
             description_dict_semantic[f"user goal-{domain}-{slot.lower()}"] = f"user goal {domain} {slot}"
 
     if db_domains:
         for domain in db_domains:
+            domain = domain.lower()
             description_dict_semantic[f"db-{domain}-entities"] = f"data base {domain} number of entities"
             description_dict_semantic[f"general-{domain}-booked"] = f"general {domain} booked"
 
     for domain in domains:
+        domain = domain.lower()
         description_dict_semantic[f"general-{domain}"] = f"domain {domain}"
 
     for act in da_voc:
         domain, intent, slot, value = act.split("-")
+        domain = domain.lower()
         description_dict_semantic["system-"+act.lower()] = f"last system act {domain} {intent} {slot} {value}"
 
     for act in da_voc_opp:
         domain, intent, slot, value = [item.lower() for item in act.split("-")]
+        domain = domain.lower()
         description_dict_semantic["user-"+act.lower()] = f"user act {domain} {intent} {slot} {value}"
 
     root_dir = os.path.dirname(os.path.abspath(__file__))

convlab/policy/vtrace_DPT/supervised/loader.py

@@ -43,6 +43,8 @@ class PolicyDataVectorizer:
             self.data[split] = []
             raw_data = data_split[split]
 
+            num = 0
+
             for data_point in raw_data:
                 state = default_state()
 
@@ -52,6 +54,7 @@ class PolicyDataVectorizer:
                     if len(data_point['context']) > 1 else {}
                 state['system_action'] = flatten_acts(last_system_act)
                 state['terminated'] = data_point['terminated']
+                if 'booked' in data_point:
                     state['booked'] = data_point['booked']
                 dialogue_act = flatten_acts(data_point['dialogue_acts'])
 
@@ -59,6 +62,9 @@ class PolicyDataVectorizer:
                 vectorized_action = self.vector.action_vectorize(dialogue_act)
                 self.data[split].append({"state": self.vector.kg_info, "action": vectorized_action, "mask": mask,
                                          "terminated": state['terminated']})
+                num += 1
+                if num > 500:
+                    break
 
             with open(os.path.join(processed_dir, '{}.pkl'.format(split)), 'wb') as f:
                 pickle.dump(self.data[split], f)

convlab/policy/vtrace_DPT/supervised/train_supervised.py

@@ -61,6 +61,9 @@ class MLE_Trainer:
             loss_a = self.policy_loop(data)
             a_loss += loss_a.item()
             loss_a.backward()
+            for p in self.policy.parameters():
+                if p.grad is not None:
+                    p.grad[p.grad != p.grad] = 0.0
             self.policy_optim.step()
 
         self.policy.eval()
@@ -162,12 +165,17 @@ if __name__ == '__main__':
     with open(os.path.join(root_directory, 'config.json'), 'r') as f:
         cfg = json.load(f)
 
+    cfg['dataset_name'] = args.dataset_name
+
     logger, tb_writer, current_time, save_path, config_save_path, dir_path, log_save_path = \
         init_logging(os.path.dirname(os.path.abspath(__file__)), "info")
     save_config(vars(args), cfg, config_save_path)
 
     set_seed(args.seed)
     logging.info(f"Seed used: {args.seed}")
+    logging.info(f"Batch size: {cfg['batchsz']}")
+    logging.info(f"Epochs: {cfg['epoch']}")
+    logging.info(f"Learning rate: {cfg['supervised_lr']}")
 
     vector = VectorNodes(dataset_name=args.dataset_name, use_masking=False, filter_state=True)
     manager = PolicyDataVectorizer(dataset_name=args.dataset_name, vector=vector)

convlab/policy/vtrace_DPT/transformer_model/EncoderDecoder.py

@@ -343,11 +343,11 @@ class EncoderDecoder(nn.Module):
         # Map the actions to action embeddings that are fed as input to decoder model
         # pad input and remove "eos" token
         padded_decoder_input = torch.stack(
-            [torch.cat([act[:-1], torch.zeros(max_length - len(act))], dim=-1) for act in action_targets], dim=0) \
+            [torch.cat([act[:-1], torch.zeros(max_length - len(act)).to(DEVICE)], dim=-1) for act in action_targets], dim=0) \
             .to(DEVICE).long()
 
         padded_action_targets = torch.stack(
-            [torch.cat([act, torch.zeros(max_length - len(act))], dim=-1) for act in action_targets], dim=0) \
+            [torch.cat([act, torch.zeros(max_length - len(act)).to(DEVICE)], dim=-1) for act in action_targets], dim=0) \
             .to(DEVICE)
 
         decoder_input = self.action_embedder.action_embeddings[padded_decoder_input]

convlab/policy/vtrace_DPT/transformer_model/action_embedder.py

@@ -40,6 +40,7 @@ class ActionEmbedder(nn.Module):
             self.action_embeddings = nn.Parameter(self.action_embeddings)
         else:
             logging.info("We use Roberta to embed actions.")
+            self.dataset_name = node_embedder.dataset_name
             self.create_action_embeddings_roberta(node_embedder)
             self.action_embeddings.requires_grad = False
             embedding_dim = 768
@@ -102,15 +103,15 @@ class ActionEmbedder(nn.Module):
 
         return action_mask.to(DEVICE)
 
-    def get_action_mask(self, domain="", intent="", start=False):
+    def get_action_mask(self, domain=None, intent="", start=False):
 
         action_mask = torch.ones(len(self.small_action_dict))
 
         # This is for predicting end of sequence token <eos>
-        if not start and not domain:
+        if not start and domain is None:
             action_mask[self.small_action_dict['eos']] = 0
 
-        if not domain:
+        if domain is None:
             #TODO: I allow all domains now for checking supervised training
             for domain in self.domain_dict:
                 if domain not in self.forbidden_domains:
@@ -136,6 +137,8 @@ class ActionEmbedder(nn.Module):
                 if valid:
                     action_mask[self.small_action_dict[slot_value]] = 0
 
+        assert not torch.equal(action_mask, torch.ones(len(self.small_action_dict)))
+
         return action_mask.to(DEVICE)
 
     def get_current_domain_mask(self, current_domains, current=True):
@@ -154,7 +157,7 @@ class ActionEmbedder(nn.Module):
     def is_valid(self, part_action):
 
         for act in self.action_dict:
-            if part_action in act:
+            if act.startswith(part_action):
                 return True
 
         return False
@@ -202,8 +205,10 @@ class ActionEmbedder(nn.Module):
         action_embeddings.append("pad")     #add the PAD token
         small_action_dict['pad'] = len(small_action_dict)
 
-        action_embeddings_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'action_embeddings.pt')
-        small_action_dict_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'small_action_dict.json')
+        action_embeddings_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                                              f'action_embeddings_{self.dataset_name}.pt')
+        small_action_dict_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                                              f'small_action_dict_{self.dataset_name}.json')
 
         if os.path.exists(action_embeddings_path):
             self.action_embeddings = torch.load(action_embeddings_path).to(DEVICE)

convlab/policy/vtrace_DPT/transformer_model/node_embedder.py

@@ -37,7 +37,7 @@ class NodeEmbedderRoberta(nn.Module):
 
         if roberta_path:
             embedded_descriptions_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
-                                                      'embedded_descriptions.pt')
+                                                      f'embedded_descriptions_{self.dataset_name}.pt')
             if os.path.exists(embedded_descriptions_path):
                 self.embedded_descriptions = torch.load(embedded_descriptions_path).to(DEVICE)
             else:
@@ -47,7 +47,7 @@ class NodeEmbedderRoberta(nn.Module):
 
         else:
             embedded_descriptions_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
-                                                      'embedded_descriptions_base.pt')
+                                                      f'embedded_descriptions_base_{self.dataset_name}.pt')
             if os.path.exists(embedded_descriptions_path):
                 self.embedded_descriptions = torch.load(embedded_descriptions_path).to(DEVICE)
             else:

convlab/policy/vtrace_DPT/transformer_model/small_action_dict_sgd.json

+{"": 0, "alarm_1": 1, "banks_1": 2, "banks_2": 3, "buses_1": 4, "buses_2": 5, "buses_3": 6, "calendar_1": 7, "events_1": 8, "events_2": 9, "events_3": 10, "flights_1": 11, "flights_2": 12, "flights_3": 13, "flights_4": 14, "homes_1": 15, "homes_2": 16, "hotels_1": 17, "hotels_2": 18, "hotels_3": 19, "hotels_4": 20, "media_1": 21, "media_2": 22, "media_3": 23, "messaging_1": 24, "movies_1": 25, "movies_2": 26, "movies_3": 27, "music_1": 28, "music_2": 29, "music_3": 30, "payment_1": 31, "rentalcars_1": 32, "rentalcars_2": 33, "rentalcars_3": 34, "restaurants_1": 35, "restaurants_2": 36, "ridesharing_1": 37, "ridesharing_2": 38, "services_1": 39, "services_2": 40, "services_3": 41, "services_4": 42, "trains_1": 43, "travel_1": 44, "weather_1": 45, "eos": 46, "goodbye": 47, "req_more": 48, "confirm": 49, "inform_count": 50, "notify_success": 51, "offer": 52, "offer_intent": 53, "request": 54, "inform": 55, "notify_failure": 56, "none-none": 57, "new_alarm_name-1": 58, "new_alarm_time-1": 59, "count-1": 60, "alarm_name-1": 61, "alarm_time-1": 62, "addalarm-1": 63, "new_alarm_time-?": 64, "account_type-1": 65, "amount-1": 66, "recipient_account_name-1": 67, "recipient_account_type-1": 68, "balance-1": 69, "transfermoney-1": 70, "account_type-?": 71, "amount-?": 72, "recipient_account_name-?": 73, "recipient_name-1": 74, "transfer_amount-1": 75, "transfer_time-1": 76, "account_balance-1": 77, "recipient_name-?": 78, "transfer_amount-?": 79, "from_location-1": 80, "leaving_date-1": 81, "leaving_time-1": 82, "to_location-1": 83, "travelers-1": 84, "from_station-1": 85, "to_station-1": 86, "transfers-1": 87, "fare-1": 88, "buybusticket-1": 89, "from_location-?": 90, "leaving_date-?": 91, "leaving_time-?": 92, "to_location-?": 93, "travelers-?": 94, "departure_date-1": 95, "departure_time-1": 96, "destination-1": 97, "fare_type-1": 98, "group_size-1": 99, "origin-1": 100, "destination_station_name-1": 101, "origin_station_name-1": 102, "price-1": 103, "departure_date-?": 104, "departure_time-?": 105, "destination-?": 106, "group_size-?": 107, "origin-?": 108, "additional_luggage-1": 109, "from_city-1": 110, "num_passengers-1": 111, "to_city-1": 112, "category-1": 113, "from_city-?": 114, "num_passengers-?": 115, "to_city-?": 116, "event_date-1": 117, "event_location-1": 118, "event_name-1": 119, "event_time-1": 120, "available_end_time-1": 121, "available_start_time-1": 122, "addevent-1": 123, "event_date-?": 124, "event_location-?": 125, "event_name-?": 126, "event_time-?": 127, "city_of_event-1": 128, "date-1": 129, "number_of_seats-1": 130, "address_of_location-1": 131, "subcategory-1": 132, "time-1": 133, "buyeventtickets-1": 134, "category-?": 135, "city_of_event-?": 136, "date-?": 137, "number_of_seats-?": 138, "city-1": 139, "number_of_tickets-1": 140, "venue-1": 141, "venue_address-1": 142, "city-?": 143, "event_type-?": 144, "number_of_tickets-?": 145, "price_per_ticket-1": 146, "airlines-1": 147, "destination_city-1": 148, "inbound_departure_time-1": 149, "origin_city-1": 150, "outbound_departure_time-1": 151, "passengers-1": 152, "return_date-1": 153, "seating_class-1": 154, "destination_airport-1": 155, "inbound_arrival_time-1": 156, "number_stops-1": 157, "origin_airport-1": 158, "outbound_arrival_time-1": 159, "refundable-1": 160, "reserveonewayflight-1": 161, "reserveroundtripflights-1": 162, "airlines-?": 163, "destination_city-?": 164, "inbound_departure_time-?": 165, "origin_city-?": 166, "outbound_departure_time-?": 167, "return_date-?": 168, "is_redeye-1": 169, "arrives_next_day-1": 170, "destination_airport_name-1": 171, "origin_airport_name-1": 172, "is_nonstop-1": 173, "destination_airport-?": 174, "origin_airport-?": 175, "property_name-1": 176, "visit_date-1": 177, "furnished-1": 178, "pets_allowed-1": 179, "phone_number-1": 180, "address-1": 181, "number_of_baths-1": 182, "number_of_beds-1": 183, "rent-1": 184, "schedulevisit-1": 185, "area-?": 186, "number_of_beds-?": 187, "visit_date-?": 188, "has_garage-1": 189, "in_unit_laundry-1": 190, "intent-?": 191, "number_of_baths-?": 192, "check_in_date-1": 193, "hotel_name-1": 194, "number_of_days-1": 195, "number_of_rooms-1": 196, "has_wifi-1": 197, "price_per_night-1": 198, "street_address-1": 199, "star_rating-1": 200, "reservehotel-1": 201, "check_in_date-?": 202, "hotel_name-?": 203, "number_of_days-?": 204, "check_out_date-1": 205, "number_of_adults-1": 206, "where_to-1": 207, "has_laundry_service-1": 208, "total_price-1": 209, "rating-1": 210, "bookhouse-1": 211, "check_out_date-?": 212, "number_of_adults-?": 213, "where_to-?": 214, "location-1": 215, "pets_welcome-1": 216, "average_rating-1": 217, "location-?": 218, "place_name-1": 219, "stay_length-1": 220, "smoking_allowed-1": 221, "stay_length-?": 222, "subtitles-1": 223, "title-1": 224, "directed_by-1": 225, "genre-1": 226, "title-2": 227, "title-3": 228, "playmovie-1": 229, "genre-?": 230, "title-?": 231, "movie_name-1": 232, "subtitle_language-1": 233, "movie_name-2": 234, "movie_name-3": 235, "rentmovie-1": 236, "starring-1": 237, "contact_name-1": 238, "contact_name-?": 239, "show_date-1": 240, "show_time-1": 241, "show_type-1": 242, "theater_name-1": 243, "buymovietickets-1": 244, "movie_name-?": 245, "show_date-?": 246, "show_time-?": 247, "show_type-?": 248, "aggregate_rating-1": 249, "cast-1": 250, "movie_title-1": 251, "percent_rating-1": 252, "playback_device-1": 253, "song_name-1": 254, "album-1": 255, "year-1": 256, "artist-1": 257, "playsong-1": 258, "song_name-?": 259, "playmedia-1": 260, "device-1": 261, "track-1": 262, "payment_method-1": 263, "private_visibility-1": 264, "receiver-1": 265, "payment_method-?": 266, "receiver-?": 267, "dropoff_date-1": 268, "pickup_date-1": 269, "pickup_location-1": 270, "pickup_time-1": 271, "type-1": 272, "car_name-1": 273, "reservecar-1": 274, "dropoff_date-?": 275, "pickup_city-?": 276, "pickup_date-?": 277, "pickup_location-?": 278, "pickup_time-?": 279, "type-?": 280, "car_type-1": 281, "car_type-?": 282, "add_insurance-1": 283, "end_date-1": 284, "start_date-1": 285, "price_per_day-1": 286, "add_insurance-?": 287, "end_date-?": 288, "start_date-?": 289, "party_size-1": 290, "restaurant_name-1": 291, "cuisine-1": 292, "has_live_music-1": 293, "price_range-1": 294, "serves_alcohol-1": 295, "reserverestaurant-1": 296, "cuisine-?": 297, "restaurant_name-?": 298, "time-?": 299, "has_seating_outdoors-1": 300, "has_vegetarian_options-1": 301, "number_of_riders-1": 302, "shared_ride-1": 303, "approximate_ride_duration-1": 304, "ride_fare-1": 305, "number_of_riders-?": 306, "shared_ride-?": 307, "ride_type-1": 308, "wait_time-1": 309, "ride_type-?": 310, "appointment_date-1": 311, "appointment_time-1": 312, "stylist_name-1": 313, "is_unisex-1": 314, "bookappointment-1": 315, "appointment_date-?": 316, "appointment_time-?": 317, "dentist_name-1": 318, "offers_cosmetic_services-1": 319, "doctor_name-1": 320, "therapist_name-1": 321, "class-1": 322, "date_of_journey-1": 323, "from-1": 324, "journey_start_time-1": 325, "to-1": 326, "trip_protection-1": 327, "total-1": 328, "gettraintickets-1": 329, "date_of_journey-?": 330, "from-?": 331, "to-?": 332, "trip_protection-?": 333, "free_entry-1": 334, "good_for_kids-1": 335, "attraction_name-1": 336, "humidity-1": 337, "wind-1": 338, "precipitation-1": 339, "temperature-1": 340, "pad": 341}
\ No newline at end of file