fix dialogpt dual usage of END_IDX

parlai/agents/hugging_face/dialogpt.py

@@ -26,6 +26,24 @@ class DialoGPTDecoder(GPT2Decoder):
     This decoder is initialized with the pretrained model from Hugging Face.
     """
 
+    def __init__(self, opt, dict):
+        super().__init__(opt, dict)
+        self.NULL_IDX, self.START_IDX, self.END_IDX = self._get_special_tokens(
+            opt, dict
+        )
+
+    @staticmethod
+    def _get_special_tokens(opt, dict):
+        null_idx = dict.null_idx
+        if (
+            opt.get('batchsize', 1) == 1
+            and not opt['add_special_tokens']
+            and null_idx == dict.end_idx
+        ):
+            # get around the dual usage of end_idx that would otherwise mask endtoken during forward pass.
+            null_idx = -1
+        return null_idx, dict.start_idx, dict.end_idx
+
     def _init_from_pretrained(self, opt):
         # load model
         model_sz = opt['gpt2_size']
@@ -38,6 +56,10 @@ class DialoGPTModel(HFGPT2Model):
     Hugging Face DialoGPT Model.
     """
 
+    def _get_special_tokens(self, opt, dict):
+        # keep it consistent between DialoGPTModel and DialoGPTDecoder on start_idx, end_idx, null_idx
+        return DialoGPTDecoder._get_special_tokens(opt, dict)
+
     def _get_decoder(self, opt, dict):
         return DialoGPTDecoder(opt, dict)
 

parlai/agents/hugging_face/gpt2.py

@@ -85,7 +85,8 @@ def forward(self, input, encoder_state, incr_state=None):
                 and int(input[0][0]) == self.START_IDX
             ):
                 # generating: ignore the start token
-                model_input = encoder_state
+                # without deep copy, the padding_idx (-1) in encoder_state can be reset to 0 with clamp_ inplace operation
+                model_input = encoder_state.clone()
             else:
                 # forced decoding: concatenate the context
                 # with the labels
@@ -108,6 +109,7 @@ def forward(self, input, encoder_state, incr_state=None):
             model_input = input[:, -1:]
             attention_mask = torch.cat([encoder_state, input], dim=-1) != self.NULL_IDX
 
+        model_input = model_input.clamp_(min=0)
         transformer_outputs = self.transformer(
             model_input,
             past=incr_state,

tests/nightly/gpu/test_dialogpt.py

@@ -6,18 +6,119 @@
 
 import unittest
 import parlai.utils.testing as testing_utils
+from parlai.core.agents import create_agent
 
 
 @testing_utils.skipUnlessGPU
 class TestDialogptModel(unittest.TestCase):
     """
     Test of DialoGPT model.
-
-    Checks that DialoGPT gets a certain performance on the integration test task.
     """
 
+    def _test_batchsize(self, batchsize, add_start_token):
+        utterances = [
+            'How is your day so far?',
+            'I hope you you have a good day.',
+            "Nice to meet you. My name is John. ",
+            "I've got a feeling we're not in Kansas anymore.",
+        ]
+        opt = {
+            'model': 'hugging_face/dialogpt',
+            'gpt2_size': 'small',
+            'text_truncate': 100,
+            'label_truncate': 20,
+            'beam_min_length': 1,
+            'inference': 'beam',
+            'beam_size': 1,
+            'add_special_tokens': True,
+            'batchsize': batchsize,
+            'add_start_token': add_start_token,
+        }
+        dialogpt = create_agent(opt)
+
+        results_single = []
+        agents = [dialogpt.clone() for _ in utterances]
+        for u, a in zip(utterances, agents):
+            a.observe({'text': u, 'episode_done': True})
+            generation = a.act()['text']
+            results_single.append(generation)
+
+        results_batched = []
+        for idx in range(len(utterances) // batchsize):
+            agents = [dialogpt.clone() for _ in range(batchsize)]
+            batch = utterances[idx * batchsize : (idx + 1) * batchsize]
+            obs = []
+            for i, a in enumerate(agents):
+                obs.append(a.observe({'text': batch[i], 'episode_done': True}))
+            generations = [x['text'] for x in dialogpt.batch_act(obs)]
+            results_batched += generations
+
+        assert results_single == results_batched
+
+    def test_batchsize(self):
+        """
+        Ensures dialogpt provides the same generation results regardless of batchsize.
+        """
+        # Test throwing the RuntimeError with add_special_tokens = False and batchsize > 1
+        with self.assertRaises(RuntimeError):
+            create_agent(
+                {
+                    'model': 'hugging_face/dialogpt',
+                    'add_special_tokens': False,
+                    'batchsize': 2,
+                }
+            )
+
+        for batchsize in [1, 2, 4]:
+            for add_start_token in [True, False]:
+                with self.subTest(
+                    f'test_batchsize with bs={batchsize} and add_start_token={add_start_token}'
+                ):
+                    self._test_batchsize(batchsize, add_start_token)
+
+    def test_start_token(self):
+        """
+        Test RuntimeError is thrown when add_start_token = True and yet add_special_tokens = False
+        """
+        with self.assertRaises(RuntimeError):
+            create_agent(
+                {
+                    'model': 'hugging_face/dialogpt',
+                    'add_special_tokens': False,
+                    'add_start_token': True,
+                }
+            )
+
+    def test_nospecialtok(self):
+        """
+        Test generation consistency for off-the-shelf dialogpt models.
+        """
+        test_cases = [
+            ("What a nice weather!", "I'm in the UK and it's raining here."),
+            ("Nice to meet you!", "Hello! I'm from the future!"),
+        ]
+        opt = {
+            'model': 'hugging_face/dialogpt',
+            'gpt2_size': 'small',
+            'text_truncate': 100,
+            'label_truncate': 20,
+            'beam_min_length': 1,
+            'inference': 'beam',
+            'beam_size': 1,
+            'add_special_tokens': False,
+            'batchsize': 1,
+        }
+        dialogpt = create_agent(opt)
+        for text, label in test_cases:
+            dialogpt.observe({'text': text, 'episode_done': True})
+            response = dialogpt.act()
+            assert response['text'] == label
+
     @testing_utils.retry(ntries=3, log_retry=True)
     def test_dialogpt(self):
+        """
+        Checks that DialoGPT gets a certain performance on the integration test task.
+        """
         valid, test = testing_utils.train_model(
             dict(
                 task='integration_tests:overfit',

tests/nightly/gpu/test_gpt2.py

@@ -36,6 +36,8 @@ def test_custom_special_tokens(self):
 
 
 class TestGpt2(unittest.TestCase):
+    # Did you implement a test for DialoGPT too if your changes affect it?
+
     def _test_batchsize(self, batchsize, add_start_token):
         utterances = [
             'Just keep swimming -',