src/bart_with_group_beam.py

from transformers.models.bart import BartForConditionalGeneration
import torch
from transformers.generation_beam_search import BeamScorer
from abc import ABC, abstractmethod
from collections import UserDict
from typing import Optional, Tuple, Union, Dict, Any
from transformers.generation_logits_process import LogitsProcessorList
from transformers.generation_utils import BeamSearchEncoderDecoderOutput,BeamSearchDecoderOnlyOutput
from torch.nn import functional as F
from transformers.file_utils import ModelOutput
import torch.nn

BeamSearchOutput = Union[BeamSearchEncoderDecoderOutput, BeamSearchDecoderOnlyOutput]


class BartForConditionalGeneration_GroupBeam(BartForConditionalGeneration):


    def beam_search(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: BeamScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        **model_kwargs,
    ) -> Union[BeamSearchOutput, torch.LongTensor]:
        r"""
        Generates sequences for models with a language modeling head using beam search decoding.

        Parameters:

            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty
                :obj:`torch.LongTensor` of shape :obj:`(1,)`.
            beam_scorer (:obj:`BeamScorer`):
                An derived instance of :class:`~transformers.BeamScorer` that defines how beam hypotheses are
                constructed, stored and sorted during generation. For more information, the documentation of
                :class:`~transformers.BeamScorer` should be read.
            logits_processor (:obj:`LogitsProcessorList`, `optional`):
                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from
                :class:`~transformers.LogitsProcessor` used to modify the prediction scores of the language modeling
                head applied at each generation step.
            max_length (:obj:`int`, `optional`, defaults to 20):
                The maximum length of the sequence to be generated.
            pad_token_id (:obj:`int`, `optional`):
                The id of the `padding` token.
            eos_token_id (:obj:`int`, `optional`):
                The id of the `end-of-sequence` token.
            output_attentions (:obj:`bool`, `optional`, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                returned tensors for more details.
            output_hidden_states (:obj:`bool`, `optional`, defaults to `False`):
                Whether or not to return trhe hidden states of all layers. See ``hidden_states`` under returned tensors
                for more details.
            output_scores (:obj:`bool`, `optional`, defaults to `False`):
                Whether or not to return the prediction scores. See ``scores`` under returned tensors for more details.
            return_dict_in_generate (:obj:`bool`, `optional`, defaults to `False`):
                Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
            model_kwargs:
                Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model. If
                model is an encoder-decoder model the kwargs should include :obj:`encoder_outputs`.

        Return:
            :class:`~transformers.generation_utilsBeamSearchDecoderOnlyOutput`,
            :class:`~transformers.generation_utils.BeamSearchEncoderDecoderOutput` or obj:`torch.LongTensor`: A
            :obj:`torch.LongTensor` containing the generated tokens (default behaviour) or a
            :class:`~transformers.generation_utils.BeamSearchDecoderOnlyOutput` if
            ``model.config.is_encoder_decoder=False`` and ``return_dict_in_generate=True`` or a
            :class:`~transformers.generation_utils.BeamSearchEncoderDecoderOutput` if
            ``model.config.is_encoder_decoder=True``.


        Examples::

            >>> from transformers import (
            ...    AutoTokenizer,
            ...    AutoModelForSeq2SeqLM,
            ...    LogitsProcessorList,
            ...    MinLengthLogitsProcessor,
            ...    BeamSearchScorer,
            ... )
            >>> import torch

            >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
            >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

            >>> encoder_input_str = "translate English to German: How old are you?"
            >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids


            >>> # lets run beam search using 3 beams
            >>> num_beams = 3
            >>> # define decoder start token ids
            >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
            >>> input_ids = input_ids * model.config.decoder_start_token_id

            >>> # add encoder_outputs to model keyword arguments
            >>> model_kwargs = {
            ...     "encoder_outputs": model.get_encoder()(encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True)
            ... }

            >>> # instantiate beam scorer
            >>> beam_scorer = BeamSearchScorer(
            ...     batch_size=1,
            ...     max_length=model.config.max_length,
            ...     num_beams=num_beams,
            ...     device=model.device,
            ... )

            >>> # instantiate logits processors
            >>> logits_processor = LogitsProcessorList([
            ...     MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
            ... ])

            >>> outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)

            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))
        """

        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        max_length = max_length if max_length is not None else self.config.max_length
        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
        output_scores = output_scores if output_scores is not None else self.config.output_scores
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
        )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and self.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

        batch_size = len(beam_scorer._beam_hyps)
        num_beams = beam_scorer.num_beams

        batch_beam_size, cur_len = input_ids.shape

        assert (
            num_beams * batch_size == batch_beam_size
        ), "Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."

        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
        beam_scores[:, 1:] = -1e9
        beam_scores = beam_scores.view((batch_size * num_beams,))

        while cur_len < max_length:
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            outputs = self(
                **model_inputs,
                return_dict=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )
            next_token_logits = outputs.logits[:, -1, :]

            # adjust tokens for Bart, *e.g.*
            next_token_logits = self.adjust_logits_during_generation(
                next_token_logits, cur_len=cur_len, max_length=max_length
            )

            next_token_scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)

            next_token_scores = logits_processor(input_ids, next_token_scores)
            next_token_scores = next_token_scores + beam_scores[:, None].expand_as(next_token_scores)

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_token_scores,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            # reshape for beam search
            vocab_size = next_token_scores.shape[-1]
            next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
            #m = torch.nn.LayerNorm(num_beams * vocab_size)
            #next_token_scores = m(next_token_scores)

            next_token_scores_group = torch.sum(next_token_scores,dim=0,keepdim=True).expand(batch_size,-1) / batch_size

            for i in range(next_token_scores.size(0)):
                '''tmin = torch.min(next_token_scores_group[i])
                for j in range(1,len(model_kwargs['decoder_ori_input_ids'][i])):
                    next_token_scores_group[i][model_kwargs['decoder_ori_input_ids'][i][j]] = tmin'''
                for t in model_kwargs['decoder_ori_input_ids'][i]:
                    for j in range(num_beams):
                    #if t not in input_ids[i] or t==1:
                        next_token_scores_group[i][j * vocab_size + t] = next_token_scores[i][j * vocab_size + t]

            next_token_scores, next_tokens = torch.topk(
                next_token_scores_group, 2 * num_beams, dim=1, largest=True, sorted=True)

            '''next_token_scores_group = next_token_scores_group.expand(batch_size,-1)
            next_tokens_group = next_tokens_group.expand(batch_size,-1)

            next_token_scores, next_tokens = torch.topk(
                next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True
            )

            for i in range(next_token_scores.size(0)):
                j1 = 0
                for j in range(next_token_scores.size(1)):
                    if next_tokens[i][j] not in model_kwargs['decoder_ori_input_ids'][i]:
                        next_tokens[i][j] = next_tokens_group[i][j1]
                        j1 += 1
            next_token_scores = next_token_scores_group

            del next_token_scores_group, next_tokens_group'''

            next_indices = next_tokens // vocab_size
            next_tokens = next_tokens % vocab_size

            # stateless
            beam_outputs = beam_scorer.process(
                input_ids,
                next_token_scores,
                next_tokens,
                next_indices,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
            )
            beam_scores = beam_outputs["next_beam_scores"]
            beam_next_tokens = beam_outputs["next_beam_tokens"]
            beam_idx = beam_outputs["next_beam_indices"]

            input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)

            cur_len = cur_len + 1

            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )
            if model_kwargs["past"] is not None:
                model_kwargs["past"] = self._reorder_cache(model_kwargs["past"], beam_idx)

            if beam_scorer.is_done:
                break

        sequence_outputs = beam_scorer.finalize(
            input_ids, beam_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id
        )

        if return_dict_in_generate:
            if not output_scores:
                sequence_outputs["sequence_scores"] = None
            if self.config.is_encoder_decoder:
                return BeamSearchEncoderDecoderOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return BeamSearchDecoderOnlyOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return sequence_outputs["sequences"]

    def group_beam_search(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: BeamScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_scores: Optional[bool] = None,
        return_dict_in_generate: Optional[bool] = None,
        **model_kwargs,
    ):
        r"""
        Generates sequences for models with a language modeling head using beam search decoding.

        Parameters:

            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                The sequence used as a prompt for the generation. If :obj:`None` the method initializes it as an empty
                :obj:`torch.LongTensor` of shape :obj:`(1,)`.
            beam_scorer (:obj:`BeamScorer`):
                An derived instance of :class:`~transformers.BeamScorer` that defines how beam hypotheses are
                constructed, stored and sorted during generation. For more information, the documentation of
                :class:`~transformers.BeamScorer` should be read.
            logits_processor (:obj:`LogitsProcessorList`, `optional`):
                An instance of :class:`~transformers.LogitsProcessorList`. List of instances of class derived from
                :class:`~transformers.LogitsProcessor` used to modify the prediction scores of the language modeling
                head applied at each generation step.
            max_length (:obj:`int`, `optional`, defaults to 20):
                The maximum length of the sequence to be generated.
            pad_token_id (:obj:`int`, `optional`):
                The id of the `padding` token.
            eos_token_id (:obj:`int`, `optional`):
                The id of the `end-of-sequence` token.
            output_attentions (:obj:`bool`, `optional`, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
                returned tensors for more details.
            output_hidden_states (:obj:`bool`, `optional`, defaults to `False`):
                Whether or not to return trhe hidden states of all layers. See ``hidden_states`` under returned tensors
                for more details.
            output_scores (:obj:`bool`, `optional`, defaults to `False`):
                Whether or not to return the prediction scores. See ``scores`` under returned tensors for more details.
            return_dict_in_generate (:obj:`bool`, `optional`, defaults to `False`):
                Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
            model_kwargs:
                Additional model specific kwargs that will be forwarded to the :obj:`forward` function of the model. If
                model is an encoder-decoder model the kwargs should include :obj:`encoder_outputs`.

        Return:
            :class:`~transformers.generation_utils.BeamSearchDecoderOnlyOutput`,
            :class:`~transformers.generation_utils.BeamSearchEncoderDecoderOutput` or obj:`torch.LongTensor`: A
            :obj:`torch.LongTensor` containing the generated tokens (default behaviour) or a
            :class:`~transformers.generation_utils.BeamSearchDecoderOnlyOutput` if
            :class:`~transformers.generation_utils.BeamSearchDecoderOnlyOutput` if
            ``model.config.is_encoder_decoder=False`` and ``return_dict_in_generate=True`` or a
            :class:`~transformers.generation_utils.BeamSearchEncoderDecoderOutput` if
            ``model.config.is_encoder_decoder=True``.

        Examples::

            >>> from transformers import (
            ...    AutoTokenizer,
            ...    AutoModelForSeq2SeqLM,
            ...    LogitsProcessorList,
            ...    MinLengthLogitsProcessor,
            ...    HammingDiversityLogitsProcessor,
            ...    BeamSearchScorer,
            ... )
            >>> import torch

            >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
            >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

            >>> encoder_input_str = "translate English to German: How old are you?"
            >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids


            >>> # lets run diverse beam search using 6 beams
            >>> num_beams = 6
            >>> # define decoder start token ids
            >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
            >>> input_ids = input_ids * model.config.decoder_start_token_id

            >>> # add encoder_outputs to model keyword arguments
            >>> model_kwargs = {
            ...     "encoder_outputs": model.get_encoder()(encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True)
            ... }

            >>> # instantiate beam scorer
            >>> beam_scorer = BeamSearchScorer(
            ...     batch_size=1,
            ...     max_length=model.config.max_length,
            ...     num_beams=num_beams,
            ...     device=model.device,
            ...     num_beam_groups=3
            ... )

            >>> # instantiate logits processors
            >>> logits_processor = LogitsProcessorList([
            ...     HammingDiversityLogitsProcessor(5.5, num_beams=6, num_beam_groups=3),
            ...     MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
            ... ])

            >>> outputs = model.group_beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)

            >>> print("Generated:", tokenizer.batch_decode(outputs, skip_special_tokens=True))
        """

        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        max_length = max_length if max_length is not None else self.config.max_length
        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
        output_scores = output_scores if output_scores is not None else self.config.output_scores
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate if return_dict_in_generate is not None else self.config.return_dict_in_generate
        )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None

        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
        if return_dict_in_generate and self.config.is_encoder_decoder:
            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
            encoder_hidden_states = (
                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
            )

        batch_size = len(beam_scorer._beam_hyps)
        num_beams = beam_scorer.num_beams
        num_beam_groups = beam_scorer.num_beam_groups
        num_sub_beams = num_beams // num_beam_groups
        device = input_ids.device

        batch_beam_size, cur_len = input_ids.shape

        assert (
            num_beams * batch_size == batch_beam_size
        ), f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."

        beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
        # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in
        # the same group don't produce same tokens everytime.
        beam_scores[:, ::num_sub_beams] = 0
        beam_scores = beam_scores.view((batch_size * num_beams,))

        while cur_len < max_length:
            # predicted tokens in cur_len step
            current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)

            # indices which will form the beams in the next time step
            reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)

            # do one decoder step on all beams of all sentences in batch
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
            outputs = self(
                **model_inputs,
                return_dict=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            for beam_group_idx in range(num_beam_groups):
                group_start_idx = beam_group_idx * num_sub_beams
                group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
                group_size = group_end_idx - group_start_idx

                # indices of beams of current group among all sentences in batch
                batch_group_indices = []

                if output_scores:
                    processed_score = torch.zeros_like(outputs.logits[:, -1, :]).half()  # .float()

                for batch_idx in range(batch_size):
                    batch_group_indices.extend(
                        [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
                    )
                group_input_ids = input_ids[batch_group_indices]

                # select outputs of beams of current group only
                next_token_logits = outputs.logits[batch_group_indices, -1, :]

                # adjust tokens for Bart, *e.g.*
                next_token_logits = self.adjust_logits_during_generation(
                    next_token_logits, cur_len=cur_len, max_length=max_length
                )

                next_token_scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * group_size, vocab_size)
                vocab_size = next_token_scores.shape[-1]

                next_token_scores = logits_processor(
                    group_input_ids, next_token_scores, current_tokens=current_tokens, beam_group_idx=beam_group_idx
                )
                next_token_scores = next_token_scores + beam_scores[batch_group_indices].unsqueeze(-1).expand_as(
                    next_token_scores
                )

                if output_scores:
                    processed_score[batch_group_indices] = next_token_scores.half()  # .float()

                # reshape for beam search
                next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)
                ###

                next_token_scores_group = torch.sum(next_token_scores, dim=0, keepdim=True).expand(batch_size,
                                                                                                   -1) / batch_size

                for i in range(next_token_scores.size(0)):
                    '''tmin = torch.min(next_token_scores_group[i])
                    for j in range(1,len(model_kwargs['decoder_ori_input_ids'][i])):
                        next_token_scores_group[i][model_kwargs['decoder_ori_input_ids'][i][j]] = tmin'''
                    for t in model_kwargs['decoder_ori_input_ids'][i]:
                        for j in range(group_size):
                            # if t not in input_ids[i] or t==1:
                            next_token_scores_group[i][j * vocab_size + t] = next_token_scores[i][j * vocab_size + t]

                next_token_scores, next_tokens = torch.topk(
                    next_token_scores_group, 2 * group_size, dim=1, largest=True, sorted=True)


                ###
                #next_token_scores, next_tokens = torch.topk(
                #    next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True
                #)

                next_indices = next_tokens // vocab_size
                next_tokens = next_tokens % vocab_size

                # stateless
                beam_outputs = beam_scorer.process(
                    group_input_ids,
                    next_token_scores,
                    next_tokens,
                    next_indices,
                    pad_token_id=pad_token_id,
                    eos_token_id=eos_token_id,
                )
                beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"]
                beam_next_tokens = beam_outputs["next_beam_tokens"]
                beam_idx = beam_outputs["next_beam_indices"]

                input_ids[batch_group_indices] = group_input_ids[beam_idx]
                group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
                current_tokens[batch_group_indices] = group_input_ids[:, -1]

                # (beam_idx // group_size) -> batch_idx
                # (beam_idx % group_size) -> offset of idx inside the group
                reordering_indices[batch_group_indices] = (
                    num_beams * (beam_idx // group_size) + group_start_idx + (beam_idx % group_size)
                )

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (processed_score,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
                    )

                if output_hidden_states:
                    decoder_hidden_states += (
                        (outputs.decoder_hidden_states,)
                        if self.config.is_encoder_decoder
                        else (outputs.hidden_states,)
                    )

            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )
            if model_kwargs["past"] is not None:
                model_kwargs["past"] = self._reorder_cache(model_kwargs["past"], reordering_indices)

            input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
            cur_len = cur_len + 1
            if beam_scorer.is_done:
                break

        sequence_outputs = beam_scorer.finalize(
            input_ids, beam_scores, next_tokens, next_indices, pad_token_id=pad_token_id, eos_token_id=eos_token_id, max_length=max_length,
        )

        if return_dict_in_generate:
            if not output_scores:
                sequence_outputs["sequence_scores"]
            if self.config.is_encoder_decoder:
                return BeamSearchEncoderDecoderOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return BeamSearchDecoderOnlyOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return sequence_outputs["sequences"]