add bart baseline

.gitattributes

+agents/bart_model/checkpoints/checkpoints_persona_chat_peacok_random/checkpoint_epoch_30/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

agents/bart_agent.py

+# Adapted from - https://github.com/Silin159/PersonaChat-BART-PeaCoK/blob/main/eval_parlai.py
+
+from typing import List, Dict
+
+import torch
+from transformers import BartTokenizer
+from tqdm.auto import tqdm
+
+from agents.bart_model.eval_utils import create_encoder_input
+from agents.bart_model.modeling_bart import BartForConditionalGeneration
+
+class BARTResponseAgent(object):
+    def __init__(self):
+        """ Load your model(s) here """
+        torch.random.manual_seed(0)
+        torch.cuda.manual_seed(0)
+
+        tokenizer_path = 'agents/bart_model/checkpoints/checkpoints_persona_chat_peacok_random'
+        checkpoint_path = 'agents/bart_model/checkpoints/checkpoints_persona_chat_peacok_random/checkpoint_epoch_30/'
+
+        self.tokenizer = BartTokenizer.from_pretrained(tokenizer_path)
+        self.model = BartForConditionalGeneration.from_pretrained(checkpoint_path)
+
+        self.query_id, self.res_id, self.latent_id, self.persona_id, self.partner_id = \
+            self.tokenizer.convert_tokens_to_ids([
+                '<query>', '<response>', '<latent>', '<persona>', '<partner>'
+            ])
+        self.bos_id = self.tokenizer.bos_token_id
+        self.eos_id = self.tokenizer.eos_token_id
+        self.pad_id = self.tokenizer.pad_token_id
+        self.sep_id = self.tokenizer.sep_token_id
+
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+        self.model = self.model.to(self.device)
+        self.model.eval()
+
+        self.turn_id = 1
+
+        print("Model loaded!!")
+
+        self.max_input_tokens = 1024
+
+    def tokenize_conversation(self, conversation):
+        def tokenize(text):
+            return self.tokenizer.convert_tokens_to_ids(
+                self.tokenizer.tokenize(text.strip(), add_prefix_space=True)
+            )
+       
+        persona = [tokenize(line.strip()) for line in conversation['persona B']]
+        # partner = [tokenize(line.strip()) for line in conversation['persona A']]
+        partner = [] # Baseline not trained with the partner personaj
+        history = [tokenize(line['text'].strip()) for line in conversation['dialogue'][:-1]]
+        return persona, partner, history
+   
+    def prepare_tensors(self, conversation):
+        persona, partner, history = self.tokenize_conversation(conversation)
+        input_ids, attention_mask, _, _ = create_encoder_input(persona,
+            partner,
+            history,
+            self.query_id, 
+            self.res_id, 
+            self.latent_id,
+            self.persona_id, 
+            self.partner_id,
+            self.sep_id, 
+            self.eos_id
+        )
+        tensor_input_ids = torch.tensor(input_ids, device=self.device)[-self.max_input_tokens:].unsqueeze(0)
+        tensor_attention_mask = torch.tensor(attention_mask, device=self.device)[-self.max_input_tokens:].unsqueeze(0)
+        return tensor_input_ids, tensor_attention_mask
+
+
+    def generate_responses(self, test_data: List[Dict]) -> List[str]:
+        """
+        You will be provided with a batch of upto 50 independent conversations
+        Return a string for every conversation
+        
+        Input 1
+        [
+            {"persona A": ..., "persona B": ... "dialogue": ... }, # conversation 1  Turn 1
+            ...
+            {"persona A": ..., "persona B": ... "dialogue": ... }  # conversation 50 Turn 1
+        ]
+
+        Model should return 50 responses for Turn 1
+
+        ...
+        Input 7
+        [
+            {"persona A": ..., "persona B": ... "dialogue": ... }, # conversation 1  Turn 7
+            ...
+            {"persona A": ..., "persona B": ... "dialogue": ... }  # conversation 50 Turn 7
+        ]
+        Model should return 50 responses for Turn 7
+
+        Note: Turn numbers will NOT be provided as input
+
+        """
+
+        all_responses = []
+
+        for conversation in tqdm(test_data):
+            tensor_input_ids, tensor_attention_mask = self.prepare_tensors(conversation)
+            with torch.no_grad():
+                out_ids = self.model.generate(
+                    input_ids=tensor_input_ids,
+                    attention_mask=tensor_attention_mask,
+                    max_length=50,
+                    num_beams=2
+                )
+            
+            response = self.tokenizer.batch_decode(
+                    out_ids,
+                    skip_special_tokens=True, 
+                    spaces_between_special_tokens=False,
+                    clean_up_tokenization_spaces=False
+            )[0].strip()
+            all_responses.append(response)
+
+        self.turn_id = self.turn_id % 7 + 1 # Turn id goes from 1 to 7
+
+        return all_responses

agents/bart_model/checkpoints/checkpoints_persona_chat_peacok_random/added_tokens.json

+{"<query>": 50265, "<response>": 50266, "<latent>": 50267, "<persona>": 50268}
\ No newline at end of file

agents/bart_model/checkpoints/checkpoints_persona_chat_peacok_random/checkpoint_epoch_30/config.json

+{
+  "_name_or_path": "facebook/bart-large",
+  "activation_dropout": 0.1,
+  "activation_function": "gelu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "BartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 0,
+  "classif_dropout": 0.1,
+  "classifier_dropout": 0.0,
+  "d_model": 1024,
+  "decoder_attention_heads": 16,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 50266,
+  "dropout": 0.1,
+  "early_stopping": true,
+  "encoder_attention_heads": 16,
+  "encoder_ffn_dim": 4096,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "max_position_embeddings": 1024,
+  "model_type": "bart",
+  "no_repeat_ngram_size": 3,
+  "normalize_before": false,
+  "num_beams": 4,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "scale_embedding": false,
+  "task_specific_params": {
+    "summarization": {
+      "length_penalty": 1.0,
+      "max_length": 128,
+      "min_length": 12,
+      "num_beams": 4
+    },
+    "summarization_cnn": {
+      "length_penalty": 2.0,
+      "max_length": 142,
+      "min_length": 56,
+      "num_beams": 4
+    },
+    "summarization_xsum": {
+      "length_penalty": 1.0,
+      "max_length": 62,
+      "min_length": 11,
+      "num_beams": 6
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.14.1",
+  "use_cache": true,
+  "vocab_size": 50269
+}

agents/bart_model/checkpoints/checkpoints_persona_chat_peacok_random/checkpoint_epoch_30/f1_beam2.txt

+FINAL F1:  0.2186FINAL BLEU:  0.01491
\ No newline at end of file

agents/bart_model/checkpoints/checkpoints_persona_chat_peacok_random/config.json

+{
+  "_name_or_path": "facebook/bart-large",
+  "activation_dropout": 0.1,
+  "activation_function": "gelu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "BartModel"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 0,
+  "classif_dropout": 0.1,
+  "classifier_dropout": 0.0,
+  "d_model": 1024,
+  "decoder_attention_heads": 16,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "decoder_start_token_id": 50266,
+  "dropout": 0.1,
+  "early_stopping": true,
+  "encoder_attention_heads": 16,
+  "encoder_ffn_dim": 4096,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "max_position_embeddings": 1024,
+  "model_type": "bart",
+  "no_repeat_ngram_size": 3,
+  "normalize_before": false,
+  "num_beams": 4,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "scale_embedding": false,
+  "task_specific_params": {
+    "summarization": {
+      "length_penalty": 1.0,
+      "max_length": 128,
+      "min_length": 12,
+      "num_beams": 4
+    },
+    "summarization_cnn": {
+      "length_penalty": 2.0,
+      "max_length": 142,
+      "min_length": 56,
+      "num_beams": 4
+    },
+    "summarization_xsum": {
+      "length_penalty": 1.0,
+      "max_length": 62,
+      "min_length": 11,
+      "num_beams": 6
+    }
+  },
+  "transformers_version": "4.14.1",
+  "use_cache": true,
+  "vocab_size": 50269
+}

agents/bart_model/checkpoints/checkpoints_persona_chat_peacok_random/special_tokens_map.json

+{"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}, "additional_special_tokens": ["<query>", "<response>", "<latent>", "<persona>"]}
\ No newline at end of file

agents/bart_model/checkpoints/checkpoints_persona_chat_peacok_random/tokenizer_config.json

+{"errors": "replace", "unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "sep_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "cls_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": {"content": "<pad>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "model_max_length": 1024, "special_tokens_map_file": null, "tokenizer_file": "/home/cutura/.cache/huggingface/transformers/d94f53c8851dcda40774f97280e634b94b721a58e71bcc152b5f51d0d49a046a.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730", "name_or_path": "facebook/bart-large", "tokenizer_class": "BartTokenizer"}
\ No newline at end of file

agents/bart_model/eval_utils.py

+# Taken as is from - https://github.com/Silin159/PersonaChat-BART-PeaCoK/blob/main/eval_utils.py
+
+from torch.nn.utils.rnn import pad_sequence
+import torch
+import numpy as np
+
+def create_encoder_input(
+    per,
+    partner,
+    history,
+    query_id, res_id, latent_id, persona_id, partner_id, sep_id, eos_id
+    ):
+    encoder_input_ids = []
+
+    per_input_ids = [latent_id] + [persona_id]
+    for x in per:
+        per_input_ids += x + [sep_id]
+    
+    partner_input_ids = [partner_id]
+    for x in partner:
+        partner_input_ids += x + [sep_id]
+
+    encoder_input_ids += per_input_ids + partner_input_ids
+
+    for i in range(len(history)):
+        if i % 2 == 0:
+            encoder_input_ids += [query_id] + history[i] + [eos_id]
+        else:
+            encoder_input_ids += [res_id] + history[i] + [eos_id]
+    attention_mask = [1] * len(encoder_input_ids)
+    per_attention_mask = [1] * len(per_input_ids)
+
+    return encoder_input_ids, attention_mask, per_input_ids, per_attention_mask
+
+
+
+def create_decoder_input(response_ids, res_id, eos_id, golden=None):
+    assert golden != None
+
+    decoder_lmlabel= response_ids + [eos_id]
+    decoder_input_ids = [res_id] + response_ids
+    decoder_cls_index = [-100] * (len(decoder_lmlabel) - 1) + [eos_id]
+    decoder_attention_mask = [1] * len(decoder_input_ids)
+
+    if golden == False:
+        decoder_lmlabel = [-100] * len(decoder_lmlabel)
+
+    assert len(decoder_lmlabel) == len(decoder_input_ids)
+    return decoder_lmlabel, decoder_input_ids, decoder_cls_index, decoder_attention_mask
+
+
+def pad_dataset(dataset, pad_id):
+    for item_name, item in dataset.items():
+        if item_name == "input_ids" or item_name == "per_input_ids":
+            item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
+                                              batch_first=True, padding_value=pad_id)
+
+            dataset[item_name] = item
+        elif item_name == "lmlabels":
+            item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
+                                batch_first=True, padding_value=-100)
+            dataset[item_name] = item
+        elif item_name == "attention_mask" or item_name == "decoder_attention_mask" or item_name == "per_attention_mask":
+            item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
+                                batch_first=True, padding_value=0)
+            dataset[item_name] = item
+        elif item_name == "decoder_input_ids":
+            item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
+                                batch_first=True, padding_value=pad_id)
+            dataset[item_name] = item
+        elif item_name == "clslabel":
+            dataset[item_name] = torch.tensor(item).view(-1,1)
+        elif item_name == "cls_index":
+            item = pad_sequence([torch.from_numpy(np.array(x)) for x in item],
+                                batch_first=True, padding_value=-100)
+            dataset[item_name] = item
+
+
+    return dataset
\ No newline at end of file

agents/user_config.py

 from agents.dummy_agent import DummyResponseAgent
+from agents.bart_agent import BARTResponseAgent
 
-UserAgent = DummyResponseAgent
+# UserAgent = DummyResponseAgent
\ No newline at end of file
+UserAgent = BARTResponseAgent
\ No newline at end of file

requirements.txt

-numpy
+numpy
\ No newline at end of file
+transformers
+torch
+accelerate
+nltk
\ No newline at end of file