#!/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.
"""
BART Module.
"""
import torch
import torch.nn.functional as F
from typing import Any, Dict, Union, List, Optional
from parlai.agents.transformer.modules import TransformerGeneratorModel


class BartModel(TransformerGeneratorModel):
    """
    BART Model.
    """

    def output(self, tensor: torch.Tensor) -> torch.Tensor:
        """
        Compute output logits.

        Override standard TGM output to _not_ prevent generation of BOS.
        """
        # project back to vocabulary
        output = F.linear(tensor, self.embeddings.weight)
        return output

    def _get_initial_forced_decoder_input(self, bsz: int, inputs: torch.LongTensor):
        """
        Return initial input to the decoder.

        Override TGA._get_initial_forced_decoder_input to seed EOS BOS.

        :param bsz:
            batchsize
        :param inputs:
            inputs to decode

        :return initial_input:
            initial input for the decoder.
        """
        tens = (
            torch.LongTensor([self.END_IDX, self.START_IDX])
            .to(inputs)
            .detach()
            .expand(bsz, 2)
        )
        return torch.cat([tens, inputs], 1)

    def reorder_decoder_incremental_state(
        self,
        incremental_state: Dict[str, Any],
        inds: Union[List[int], torch.LongTensor],
    ) -> Optional[Dict[str, Any]]:
        """
        Incremental state is weird to handle when we seed decoder with two inputs
        initially.

        We leave as a future exercise.
        """
        return None

#!/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.
"""
BART Module.
"""
import torch
import torch.nn.functional as F
from typing import Any, Dict, Union, List, Optional
from parlai.agents.transformer.modules import TransformerGeneratorModel


class BartModel(TransformerGeneratorModel):
    """
    BART Model.
    """

    def output(self, tensor: torch.Tensor) -> torch.Tensor:
        """
        Compute output logits.

        Override standard TGM output to _not_ prevent generation of BOS.
        """
        # project back to vocabulary
        output = F.linear(tensor, self.embeddings.weight)
        return output

    def _get_initial_forced_decoder_input(self, bsz: int, inputs: torch.LongTensor):
        """
        Return initial input to the decoder.

        Override TGA._get_initial_forced_decoder_input to seed EOS BOS.

        :param bsz:
            batchsize
        :param inputs:
            inputs to decode

        :return initial_input:
            initial input for the decoder.
        """
        tens = (
            torch.LongTensor([self.END_IDX, self.START_IDX])
            .to(inputs)
            .detach()
            .expand(bsz, 2)
        )
        return torch.cat([tens, inputs], 1)

    def reorder_decoder_incremental_state(
        self,
        incremental_state: Dict[str, Any],
        inds: Union[List[int], torch.LongTensor],
    ) -> Optional[Dict[str, Any]]:
        """
        Incremental state is weird to handle when we seed decoder with two inputs
        initially.
        """
        # we only have this method called when it's actually being used
        assert incremental_state is not None
        assert len(incremental_state) > 0

        for incr_state_l in incremental_state.values():
            assert 'self_attn' in incr_state_l
            assert 'prev_mask' in incr_state_l['self_attn']
            self_attn_mask = incr_state_l['self_attn']['prev_mask']
            # check this is on the very first run with incremental state
            if self_attn_mask.ndim == 3 and tuple(self_attn_mask.shape[1:]) == (2, 2):
                # cut off the inappropriate incremental state
                incr_state_l['self_attn']['prev_mask'] = self_attn_mask[:, -1:, :]

        return super().reorder_decoder_incremental_state(incremental_state, inds)

Implement BART incremental decoding (#3231)