Decoder-Only Transformer

parlai/agents/hugging_face/gpt2.py

@@ -318,7 +318,7 @@ def build_model(self, states=None):
     def _encoder_input(self, batch):
         return (batch.text_vec,)
 
-    def _pad_tensor(self, items):
+    def _pad_tensor(self, items, is_label=False):
         """
         Override to always set fp16friendly to False and left_pad to True.
         """

parlai/agents/rag/modules.py

@@ -89,7 +89,10 @@ def __init__(self, opt, dictionary, retriever_shared=None):
             padding_idx=self.pad_idx,
         )
         self.seq2seq_decoder = self.build_decoder(
-            opt, embedding=self.embeddings, padding_idx=self.pad_idx
+            opt,
+            embedding=self.embeddings,
+            dictionary=dictionary,
+            padding_idx=self.pad_idx,
         )
 
     @classmethod
@@ -121,7 +124,9 @@ def build_decoder(
         **kwargs,
     ):
         if decoder_class is None:
-            return RagDecoder(opt=opt, embedding=embedding, n_positions=n_positions)
+            return RagDecoder(
+                opt=opt, embedding=embedding, n_positions=n_positions, **kwargs
+            )
         else:
             return decoder_class(opt, *args, **kwargs)
 

parlai/agents/transformer/decoder.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Optional
+
+from parlai.agents.transformer.transformer import add_common_cmdline_args
+from parlai.core.opt import Opt
+from parlai.core.params import ParlaiParser
+from parlai.core.torch_generator_agent import TorchGeneratorAgent
+from parlai.utils.logging import logging
+from parlai.utils.misc import recursive_getattr
+from parlai.utils.torch import padded_tensor
+
+from .modules import (
+    PassThroughEncoder,
+    TransformerDecoderOnly,
+    TransformerGeneratorModel,
+)
+
+
+class DecoderAgent(TorchGeneratorAgent):
+    """
+    DecoderOnlyAgent.
+
+    Implementation of TorchGeneratorAgent, where the model is a Decoder-Only
+    Transformer.
+    """
+
+    @classmethod
+    def add_cmdline_args(
+        cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None
+    ) -> ParlaiParser:
+        """
+        Add command-line arguments specifically for this agent.
+        """
+        agent = parser.add_argument_group('Decoder-Only Transformer Arguments')
+        add_common_cmdline_args(agent)
+        cls.dictionary_class().add_cmdline_args(parser, partial_opt=partial_opt)
+
+        super().add_cmdline_args(parser, partial_opt=partial_opt)
+        return agent
+
+    def build_model(self, states=None):
+        """
+        Override of ``TorchAgent.build_model``.
+        """
+        assert (
+            self.opt['n_encoder_layers'] == -1
+        ), "Decoder-only model cannot have encoder layers."
+        wrapped_class = TransformerGeneratorModel.with_components(
+            encoder=PassThroughEncoder, decoder=TransformerDecoderOnly
+        )
+        return wrapped_class(self.opt, self.dict)
+
+    def _pad_tensor(self, items, is_label=False):
+        """
+        Override of ``TorchAgent._pad_tensor``.
+
+        Pads context tensor on the left and label tensor on the right, such that when
+        they are concatenated the example meets in the middle to form a continuous
+        sequence.
+        """
+        return padded_tensor(
+            items,
+            pad_idx=self.NULL_IDX,
+            left_padded=(not is_label),
+            fp16friendly=self.fp16,
+        )
+
+    def _resize_token_embeddings(self, state_dict, msg=None):
+        """
+        Resize the token embeddings when adding extra special tokens.
+        """
+        # map extra special tokens carefully
+        new_size = self.model.embeddings.weight.size()[0]
+        orig_size = state_dict['embeddings.weight'].size()[0]
+        logging.info(f'Resizing token embeddings from {orig_size} to {new_size}')
+        if new_size <= orig_size:
+            # new size should be greater than original size,
+            # as we are adding special tokens
+            raise RuntimeError(msg)
+
+        for emb_weights in ['embeddings.weight', 'decoder.embeddings.weight']:
+            # get new_embs
+            old_embs = state_dict[emb_weights]
+            new_embs = recursive_getattr(self.model, emb_weights).to(old_embs.device)
+            # copy over old weights
+            new_embs.data[:orig_size, :] = old_embs.data[:orig_size, :]
+            # reset in state dict
+            state_dict[emb_weights] = new_embs
+
+        return state_dict

parlai/agents/transformer/modules/__init__.py

@@ -11,8 +11,17 @@
 )
 from .attention import BasicAttention, MultiHeadAttention  # noqa: F401
 from .ffn import TransformerFFN  # noqa: F401
-from .encoder import TransformerEncoder, TransformerEncoderLayer  # noqa: F401
-from .decoder import TransformerDecoder, TransformerDecoderLayer  # noqa: F401
+from .encoder import (  # noqa: F401
+    PassThroughEncoder,
+    TransformerEncoder,
+    TransformerEncoderLayer,
+)
+from .decoder import (  # noqa: F401
+    TransformerDecoder,
+    TransformerDecoderLayer,
+    TransformerDecoderOnly,
+    TransformerDecoderOnlyLayer,
+)
 from .generator import TransformerGeneratorModel  # noqa: F401
 from .wrappers import TransformerLinearWrapper, TransformerResponseWrapper  # noqa: F401
 from .mem_net import TransformerMemNetModel  # noqa: F401

parlai/agents/transformer/modules/attention.py

@@ -15,6 +15,7 @@
 import torch.nn.functional as F
 
 from parlai.core.opt import Opt
+from parlai.core.params import default
 from parlai.utils.torch import neginf
 
 
@@ -98,14 +99,8 @@ def __init__(
     ):
         super(MultiHeadAttention, self).__init__()
 
-        def _default(val, default):
-            """
-            shorthand for explicit None check for optional arguments.
-            """
-            return val if val is not None else default
-
-        n_heads = _default(n_heads, opt['n_heads'])
-        dim = _default(dim, opt['embedding_size'])
+        n_heads = default(n_heads, opt['n_heads'])
+        dim = default(dim, opt['embedding_size'])
 
         self.n_heads = n_heads
         self.dim = dim
@@ -224,9 +219,13 @@ def prepare_head(tensor):
             if static_kv:
                 mask = incr_state['prev_mask']
             else:
-                mask = torch.cat([incr_state['prev_mask'], mask], dim=2)
-                # Prepend along the key_len dimension (analogous to
-                # incr_state['prev_key'])
+                # Mask will be of size (B x query_len x key_len)
+                # During incremental decoding the query will only represent the next token,
+                # whereas the key/value will represent the entire sequence thus far.
+                # In such a case, we only want to look at the last element of the mask in the query dimension.
+                prev_mask = incr_state['prev_mask'][:, -query_len:, :]
+                mask = torch.cat([prev_mask, mask], dim=2)
+                # Prepend along the key_len dimension (analogous to incr_state['prev_key'])
 
         # Save new incremental states. We reshape to allow for reordering along batch
         # dimension.