fairseq/examples/MMPT/mmpt/models/mmfusionnlg.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Copyright (c) Facebook, Inc. All Rights Reserved


import torch

from torch.nn import functional as F

from typing import Optional, Iterable

try:
    from transformers import BertPreTrainedModel
    from transformers.modeling_bert import BertOnlyMLMHead

    from transformers.file_utils import ModelOutput
    from transformers.modeling_outputs import CausalLMOutput
    from transformers.generation_utils import (
        BeamHypotheses,
        top_k_top_p_filtering
    )
except ImportError:
    pass

from .mmfusion import MMFusion
from .transformermodel import MMBertModel
from ..modules import VideoTokenMLP


class MMFusionNLG(MMFusion):
    def __init__(self, config, **kwargs):
        super().__init__(config)
        if config.model.max_decode_length is not None:
            self.max_length = min(
                config.model.max_decode_length,
                config.dataset.max_len - config.dataset.max_video_len - 3
            )
        else:
            self.max_length = \
                config.dataset.max_len - config.dataset.max_video_len - 3
        self.gen_param = config.gen_param if config.gen_param is not None \
            else {}

    def forward(
        self,
        caps,
        cmasks,
        vfeats,
        vmasks,
        attention_mask,
        video_label=None,
        text_label=None,
        **kwargs
    ):
        """use pre-trained LM header for generation."""
        attention_mask, token_type_ids = self._mm_on_the_fly(
            cmasks, vmasks, attention_mask)

        outputs = self.mm_encoder(
            input_ids=caps,
            input_video_embeds=vfeats,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            masked_lm_labels=text_label,
        )
        return {"logits": outputs[0]}

    @torch.no_grad()
    def generate(
        self,
        caps, cmasks, vfeats, vmasks,
        attention_mask=None,
        bos_token_id=None,
        eos_token_id=None,
        **kwargs
    ):
        # a simplified interface from
        # https://huggingface.co/transformers/v3.4.0/_modules/transformers/generation_utils.html#GenerationMixin.generate

        # caps now only have
        # [CLS], [SEP] (for video) and [CLS] (as bos_token)
        assert caps.size(1) == 3

        attention_mask, token_type_ids = self._mm_on_the_fly(
            cmasks, vmasks, attention_mask)

        output = self.mm_encoder.generate(
            input_ids=caps,
            input_video_embeds=vfeats,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            max_length=self.max_length,
            **self.gen_param
        )
        return output


class MMBertForNLG(BertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.bert = MMBertModel(config)
        self.videomlp = VideoTokenMLP(config)
        # we do not use `BertGenerationOnlyLMHead`
        # because we can reuse pretraining.
        self.cls = BertOnlyMLMHead(config)
        self.hidden_size = config.hidden_size
        self.init_weights()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def forward(
        self,
        input_ids=None,
        input_video_embeds=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        masked_lm_labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        # similar to MMBertForMFMMLM without MFM.
        video_tokens = self.videomlp(input_video_embeds)
        outputs = self.bert(
            input_ids,
            video_tokens,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        prediction_scores = None
        if masked_lm_labels is not None:
            text_offset = input_video_embeds.size(1) + 1  # [CLS]
            # recover caps format: [CLS] [SEP] text [SEP]
            text_sequence_output = torch.cat(
                [sequence_output[:, :1], sequence_output[:, text_offset:]],
                dim=1
            )

            # only compute select tokens to training to speed up.
            hidden_size = text_sequence_output.size(-1)
            # masked_lm_labels = masked_lm_labels.reshape(-1)
            labels_mask = masked_lm_labels != -100

            selected_text_output = text_sequence_output.masked_select(
                labels_mask.unsqueeze(-1)
            ).view(-1, hidden_size)
            prediction_scores = self.cls(selected_text_output)

        if not return_dict:
            output = (
                prediction_scores,
            ) + outputs[2:]
            return output

        # for generation.
        text_offset = input_video_embeds.size(1) + 2  # [CLS]
        text_sequence_output = sequence_output[:, text_offset:]
        prediction_scores = self.cls(text_sequence_output)
        return CausalLMOutput(
            loss=None,
            logits=prediction_scores,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        input_video_embeds,
        attention_mask=None,
        token_type_ids=None,
        **model_kwargs
    ):
        # must return a dictionary.
        seq_len = input_ids.size(1) + input_video_embeds.size(1)
        if attention_mask is not None:
            if len(attention_mask.size()) == 4:
                attention_mask = attention_mask[:, :, :seq_len, :seq_len]
            elif len(attention_mask.size()) == 3:
                attention_mask = attention_mask[:, :seq_len, :seq_len]
            else:
                attention_mask = attention_mask[:, :seq_len]
        if token_type_ids is not None:
            token_type_ids = token_type_ids[:, :seq_len]

        return {
            "input_ids": input_ids,
            "input_video_embeds": input_video_embeds,
            "attention_mask": attention_mask,
            "token_type_ids": token_type_ids,
        }

    @torch.no_grad()
    def generate(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        max_length: Optional[int] = None,
        min_length: Optional[int] = None,
        do_sample: Optional[bool] = None,
        early_stopping: Optional[bool] = None,
        num_beams: Optional[int] = None,
        temperature: Optional[float] = None,
        top_k: Optional[int] = None,
        top_p: Optional[float] = None,
        repetition_penalty: Optional[float] = None,
        bad_words_ids: Optional[Iterable[int]] = None,
        bos_token_id: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[int] = None,
        length_penalty: Optional[float] = None,
        no_repeat_ngram_size: Optional[int] = None,
        num_return_sequences: Optional[int] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        decoder_start_token_id: Optional[int] = None,
        use_cache: Optional[bool] = None,
        **model_kwargs
    ) -> torch.LongTensor:
        r"""
        Generates sequences for models with a language modeling head. The method currently supports greedy decoding,
        beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling.
        Adapted in part from `Facebook's XLM beam search code
        <https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529>`__.
        Apart from :obj:`input_ids` and :obj:`attention_mask`, all the arguments below will default to the value of the
        attribute of the same name inside the :class:`~transformers.PretrainedConfig` of the model. The default values
        indicated are the default values of those config.
        Most of these parameters are explained in more detail in `this blog post
        <https://huggingface.co/blog/how-to-generate>`__.
        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,)`.
            decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                initial input_ids for the decoder of encoder-decoder type models. If :obj:`None` then only
                decoder_start_token_id is passed as the first token to the decoder.
            max_length (:obj:`int`, `optional`, defaults to 20):
                The maximum length of the sequence to be generated.
            min_length (:obj:`int`, `optional`, defaults to 10):
                The minimum length of the sequence to be generated.
            do_sample (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to use sampling ; use greedy decoding otherwise.
            early_stopping (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether to stop the beam search when at least ``num_beams`` sentences are finished per batch or not.
            num_beams (:obj:`int`, `optional`, defaults to 1):
                Number of beams for beam search. 1 means no beam search.
            temperature (:obj:`float`, `optional`, defaults tp 1.0):
                The value used to module the next token probabilities.
            top_k (:obj:`int`, `optional`, defaults to 50):
                The number of highest probability vocabulary tokens to keep for top-k-filtering.
            top_p (:obj:`float`, `optional`, defaults to 1.0):
                If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or
                higher are kept for generation.
            repetition_penalty (:obj:`float`, `optional`, defaults to 1.0):
                The parameter for repetition penalty. 1.0 means no penalty. See `this paper
                <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details.
            pad_token_id (:obj:`int`, `optional`):
                The id of the `padding` token.
            bos_token_id (:obj:`int`, `optional`):
                The id of the `beginning-of-sequence` token.
            eos_token_id (:obj:`int`, `optional`):
                The id of the `end-of-sequence` token.
            length_penalty (:obj:`float`, `optional`, defaults to 1.0):
                Exponential penalty to the length. 1.0 means no penalty.
                Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in
                order to encourage the model to produce longer sequences.
            no_repeat_ngram_size (:obj:`int`, `optional`, defaults to 0):
                If set to int > 0, all ngrams of that size can only occur once.
            bad_words_ids(:obj:`List[int]`, `optional`):
                List of token ids that are not allowed to be generated. In order to get the tokens of the words that
                should not appear in the generated text, use :obj:`tokenizer.encode(bad_word, add_prefix_space=True)`.
            num_return_sequences(:obj:`int`, `optional`, defaults to 1):
                The number of independently computed returned sequences for each element in the batch.
            attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                Mask to avoid performing attention on padding token indices. Mask values are in ``[0, 1]``, 1 for
                tokens that are not masked, and 0 for masked tokens.
                If not provided, will default to a tensor the same shape as :obj:`input_ids` that masks the pad token.
                `What are attention masks? <../glossary.html#attention-mask>`__
            decoder_start_token_id (:obj:`int`, `optional`):
                If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token.
            use_cache: (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not the model should use the past last key/values attentions (if applicable to the model) to
                speed up decoding.
            model_kwargs:
                Additional model specific kwargs will be forwarded to the :obj:`forward` function of the model.
        Return:
            :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`:
            The generated sequences. The second dimension (sequence_length) is either equal to :obj:`max_length` or
            shorter if all batches finished early due to the :obj:`eos_token_id`.
        Examples::
            tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
            model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
            outputs = model.generate(max_length=40)  # do greedy decoding
            print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
            tokenizer = AutoTokenizer.from_pretrained('openai-gpt')   # Initialize tokenizer
            model = AutoModelWithLMHead.from_pretrained('openai-gpt')    # Download model and configuration from S3 and cache.
            input_context = 'The dog'
            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
            outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5)  # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
            for i in range(3): #  3 output sequences were generated
                print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
            tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
            model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
            input_context = 'The dog'
            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
            outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True)  # generate 3 candidates using sampling
            for i in range(3): #  3 output sequences were generated
                print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
            tokenizer = AutoTokenizer.from_pretrained('ctrl')   # Initialize tokenizer
            model = AutoModelWithLMHead.from_pretrained('ctrl')    # Download model and configuration from S3 and cache.
            input_context = 'Legal My neighbor is'  # "Legal" is one of the control codes for ctrl
            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
            outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2)  # generate sequences
            print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
            tokenizer = AutoTokenizer.from_pretrained('gpt2')   # Initialize tokenizer
            model = AutoModelWithLMHead.from_pretrained('gpt2')    # Download model and configuration from S3 and cache.
            input_context = 'My cute dog'  # "Legal" is one of the control codes for ctrl
            bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]
            input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
            outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)  # generate sequences without allowing bad_words to be generated
        """

        # We cannot generate if the model does not have a LM head
        if self.get_output_embeddings() is None:
            raise AttributeError(
                "You tried to generate sequences with a model that does not have a LM Head."
                "Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`, `XLMWithLMHeadModel`, `BartForConditionalGeneration` )"
            )

        max_length = max_length if max_length is not None else self.config.max_length
        min_length = min_length if min_length is not None else self.config.min_length
        do_sample = do_sample if do_sample is not None else self.config.do_sample
        early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        num_beams = num_beams if num_beams is not None else self.config.num_beams
        temperature = temperature if temperature is not None else self.config.temperature
        top_k = top_k if top_k is not None else self.config.top_k
        top_p = top_p if top_p is not None else self.config.top_p
        repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
        bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
        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
        length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
        no_repeat_ngram_size = (
            no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
        )
        bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
        num_return_sequences = (
            num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
        )
        decoder_start_token_id = (
            decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
        )

        if input_ids is not None:
            batch_size = input_ids.shape[0]  # overriden by the input batch_size
        else:
            batch_size = 1

        assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
        assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
        assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
        assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
        assert isinstance(use_cache, bool), "`use_cache` should be a boolean."
        assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
        assert temperature > 0, "`temperature` should be strictly positive."
        assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
        assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1."
        assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1."
        assert input_ids is not None or (
            isinstance(bos_token_id, int) and bos_token_id >= 0
        ), "If input_ids is not defined, `bos_token_id` should be a positive integer."
        assert pad_token_id is None or (
            isinstance(pad_token_id, int) and (pad_token_id >= 0)
        ), "`pad_token_id` should be a positive integer."
        assert (eos_token_id is None) or (
            isinstance(eos_token_id, int) and (eos_token_id >= 0)
        ), "`eos_token_id` should be a positive integer."
        assert length_penalty > 0, "`length_penalty` should be strictly positive."
        assert (
            isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0
        ), "`no_repeat_ngram_size` should be a positive integer."
        assert (
            isinstance(num_return_sequences, int) and num_return_sequences > 0
        ), "`num_return_sequences` should be a strictly positive integer."
        assert (
            bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)
        ), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated"

        if input_ids is None:
            assert isinstance(bos_token_id, int) and bos_token_id >= 0, (
                "you should either supply a context to complete as `input_ids` input "
                "or a `bos_token_id` (integer >= 0) as a first token to start the generation."
            )
            input_ids = torch.full(
                (batch_size, 1),
                bos_token_id,
                dtype=torch.long,
                device=next(self.parameters()).device,
            )
        else:
            assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)."

        # not allow to duplicate outputs when greedy decoding
        if do_sample is False:
            if num_beams == 1:
                # no_beam_search greedy generation conditions
                assert (
                    num_return_sequences == 1
                ), "Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1"

            else:
                # beam_search greedy generation conditions
                assert (
                    num_beams >= num_return_sequences
                ), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences"

        # create attention mask if necessary
        # TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140
        if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids):
            attention_mask = input_ids.ne(pad_token_id).long()
        elif attention_mask is None:
            attention_mask = input_ids.new_ones(input_ids.shape)

        # set pad_token_id to eos_token_id if not set. Important that this is done after
        # attention_mask is created
        if pad_token_id is None and eos_token_id is not None:
            print(
                "Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id)
            )
            pad_token_id = eos_token_id

        # vocab size
        if hasattr(self.config, "vocab_size"):
            vocab_size = self.config.vocab_size
        elif (
            self.config.is_encoder_decoder
            and hasattr(self.config, "decoder")
            and hasattr(self.config.decoder, "vocab_size")
        ):
            vocab_size = self.config.decoder.vocab_size
        else:
            raise ValueError("either self.config.vocab_size or self.config.decoder.vocab_size needs to be defined")

        # set effective batch size and effective batch multiplier according to do_sample
        if do_sample:
            effective_batch_size = batch_size * num_return_sequences
            effective_batch_mult = num_return_sequences
        else:
            effective_batch_size = batch_size
            effective_batch_mult = 1

        if self.config.is_encoder_decoder:
            if decoder_start_token_id is None:
                # see if BOS token can be used for decoder_start_token_id
                if bos_token_id is not None:
                    decoder_start_token_id = bos_token_id
                elif (
                    hasattr(self.config, "decoder")
                    and hasattr(self.config.decoder, "bos_token_id")
                    and self.config.decoder.bos_token_id is not None
                ):
                    decoder_start_token_id = self.config.decoder.bos_token_id
                else:
                    raise ValueError(
                        "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
                    )

            assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
            assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)

            # get encoder and store encoder outputs
            encoder = self.get_encoder()
            encoder_outputs: ModelOutput = encoder(input_ids, attention_mask=attention_mask, return_dict=True)

        # Expand input ids if num_beams > 1 or num_return_sequences > 1
        if num_return_sequences > 1 or num_beams > 1:
            # TODO: make this a call-back function.
            # input_ids=caps,
            # input_video_embeds=vfeats,
            # attention_mask=attention_mask,
            # token_type_ids=token_type_ids,
            input_video_embeds = model_kwargs.pop("input_video_embeds", None)
            token_type_ids = model_kwargs.pop("token_type_ids", None)

            input_ids_len = input_ids.shape[-1]
            input_ids = input_ids.unsqueeze(1).expand(
                 batch_size, effective_batch_mult * num_beams, input_ids_len)

            input_video_embeds_len, input_video_embeds_hidden = input_video_embeds.size(1), input_video_embeds.size(2)
            input_video_embeds = input_video_embeds.unsqueeze(1).expand(
                batch_size, effective_batch_mult * num_beams, input_video_embeds_len, input_video_embeds_hidden)

            attention_mask_from_len, attention_mask_to_len = attention_mask.size(1), attention_mask.size(2)
            attention_mask = attention_mask.unsqueeze(1).expand(
                batch_size, effective_batch_mult * num_beams, attention_mask_from_len, attention_mask_to_len
            )

            token_type_ids_len = token_type_ids.size(1)
            token_type_ids = token_type_ids.unsqueeze(1).expand(
                batch_size, effective_batch_mult * num_beams, token_type_ids_len
            )

            # contiguous ...
            input_ids = input_ids.contiguous().view(
                effective_batch_size * num_beams, input_ids_len
            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)

            input_video_embeds = input_video_embeds.contiguous().view(
                effective_batch_size * num_beams, input_video_embeds_len, input_video_embeds_hidden)

            attention_mask = attention_mask.contiguous().view(
                effective_batch_size * num_beams, attention_mask_from_len, attention_mask_to_len
            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)

            token_type_ids = token_type_ids.contiguous().view(
                effective_batch_size * num_beams, token_type_ids_len
            )

            model_kwargs["input_video_embeds"] = input_video_embeds
            model_kwargs["token_type_ids"] = token_type_ids

        if self.config.is_encoder_decoder:
            device = next(self.parameters()).device
            if decoder_input_ids is not None:
                # give initial decoder input ids
                input_ids = decoder_input_ids.repeat(effective_batch_size * num_beams, 1).to(device)
            else:
                # create empty decoder input_ids
                input_ids = torch.full(
                    (effective_batch_size * num_beams, 1),
                    decoder_start_token_id,
                    dtype=torch.long,
                    device=device,
                )
            cur_len = input_ids.shape[-1]

            assert (
                batch_size == encoder_outputs.last_hidden_state.shape[0]
            ), f"expected encoder_outputs.last_hidden_state to have 1st dimension bs={batch_size}, got {encoder_outputs.last_hidden_state.shape[0]} "

            # expand batch_idx to assign correct encoder output for expanded input_ids (due to num_beams > 1 and num_return_sequences > 1)
            expanded_batch_idxs = (
                torch.arange(batch_size)
                .view(-1, 1)
                .repeat(1, num_beams * effective_batch_mult)
                .view(-1)
                .to(input_ids.device)
            )

            # expand encoder_outputs
            encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
                0, expanded_batch_idxs
            )

            # save encoder_outputs in `model_kwargs`
            model_kwargs["encoder_outputs"] = encoder_outputs

        else:
            cur_len = input_ids.shape[-1]

        assert (
            cur_len < max_length
        ), f"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or `config.max_length = ...`"

        if num_beams > 1:
            output = self._generate_beam_search(
                input_ids,
                cur_len=cur_len,
                max_length=max_length,
                min_length=min_length,
                do_sample=do_sample,
                early_stopping=early_stopping,
                temperature=temperature,
                top_k=top_k,
                top_p=top_p,
                repetition_penalty=repetition_penalty,
                no_repeat_ngram_size=no_repeat_ngram_size,
                bad_words_ids=bad_words_ids,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                batch_size=effective_batch_size,
                num_return_sequences=num_return_sequences,
                length_penalty=length_penalty,
                num_beams=num_beams,
                vocab_size=vocab_size,
                attention_mask=attention_mask,
                use_cache=use_cache,
                model_kwargs=model_kwargs,
            )
        else:
            output = self._generate_no_beam_search(
                input_ids,
                cur_len=cur_len,
                max_length=max_length,
                min_length=min_length,
                do_sample=do_sample,
                temperature=temperature,
                top_k=top_k,
                top_p=top_p,
                repetition_penalty=repetition_penalty,
                no_repeat_ngram_size=no_repeat_ngram_size,
                bad_words_ids=bad_words_ids,
                pad_token_id=pad_token_id,
                eos_token_id=eos_token_id,
                batch_size=effective_batch_size,
                attention_mask=attention_mask,
                use_cache=use_cache,
                model_kwargs=model_kwargs,
            )

        return output

    def _generate_beam_search(
        self,
        input_ids,
        cur_len,
        max_length,
        min_length,
        do_sample,
        early_stopping,
        temperature,
        top_k,
        top_p,
        repetition_penalty,
        no_repeat_ngram_size,
        bad_words_ids,
        pad_token_id,
        eos_token_id,
        batch_size,
        num_return_sequences,
        length_penalty,
        num_beams,
        vocab_size,
        attention_mask,
        use_cache,
        model_kwargs,
    ):
        """Generate sequences for each example with beam search."""

        # generated hypotheses
        generated_hyps = [
            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
            for _ in range(batch_size)
        ]

        # scores for each sentence in the beam
        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)

        # for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
        if do_sample is False:
            beam_scores[:, 1:] = -1e9
        beam_scores = beam_scores.view(-1)  # shape (batch_size * num_beams,)

        # cache compute states
        past = None

        # done sentences
        done = [False for _ in range(batch_size)]

        while cur_len < max_length:
            model_inputs = self.prepare_inputs_for_generation(
                input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_kwargs
            )
            outputs = self(**model_inputs, return_dict=True)  # (batch_size * num_beams, cur_len, vocab_size)
            next_token_logits = outputs.logits[:, -1, :]  # (batch_size * num_beams, vocab_size)

            # if model has past, then set the past variable to speed up decoding
            if "past_key_values" in outputs:
                past = outputs.past_key_values
            elif "mems" in outputs:
                past = outputs.mems

            if self.config.is_encoder_decoder and do_sample is False:
                # TODO (PVP) still a bit hacky here - there might be a better solution
                next_token_logits = self.adjust_logits_during_generation(
                    next_token_logits, cur_len=cur_len, max_length=max_length
                )

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

            scores = self.postprocess_next_token_scores(
                scores=scores,
                input_ids=input_ids,
                no_repeat_ngram_size=no_repeat_ngram_size,
                bad_words_ids=bad_words_ids,
                cur_len=cur_len,
                min_length=min_length,
                max_length=max_length,
                eos_token_id=eos_token_id,
                repetition_penalty=repetition_penalty,
                batch_size=batch_size,
                num_beams=num_beams,
            )

            assert scores.shape == (batch_size * num_beams, vocab_size), "Shapes of scores: {} != {}".format(
                scores.shape, (batch_size * num_beams, vocab_size)
            )

            if do_sample:
                _scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)
                # Temperature
                if temperature != 1.0:
                    _scores = _scores / temperature
                # Top-p/top-k filtering
                _scores = top_k_top_p_filtering(
                    _scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
                )  # (batch_size * num_beams, vocab_size)
                # re-organize to group the beam together to sample from all beam_idxs
                _scores = _scores.contiguous().view(
                    batch_size, num_beams * vocab_size
                )  # (batch_size, num_beams * vocab_size)

                # Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
                probs = F.softmax(_scores, dim=-1)
                next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)  # (batch_size, num_beams * 2)
                # Compute next scores
                next_scores = torch.gather(_scores, -1, next_tokens)  # (batch_size, num_beams * 2)
                # sort the sampled vector to make sure that the first num_beams samples are the best
                next_scores, next_scores_indices = torch.sort(next_scores, descending=True, dim=1)
                next_tokens = torch.gather(next_tokens, -1, next_scores_indices)  # (batch_size, num_beams * 2)

            else:
                next_scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)

                # re-organize to group the beam together (we are keeping top hypothesis accross beams)
                next_scores = next_scores.view(
                    batch_size, num_beams * vocab_size
                )  # (batch_size, num_beams * vocab_size)

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

            assert next_scores.size() == next_tokens.size() == (batch_size, 2 * num_beams)

            # next batch beam content
            next_batch_beam = []

            # for each sentence
            for batch_idx in range(batch_size):

                # if we are done with this sentence, add a pad token
                if done[batch_idx]:
                    assert (
                        len(generated_hyps[batch_idx]) >= num_beams
                    ), "Batch can only be done if at least {} beams have been generated".format(num_beams)
                    assert (
                        eos_token_id is not None and pad_token_id is not None
                    ), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined"
                    next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams)  # pad the batch
                    continue

                # next sentence beam content, this will get added to next_batch_beam
                next_sent_beam = []

                # next tokens for this sentence
                for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
                    zip(next_tokens[batch_idx], next_scores[batch_idx])
                ):
                    # get beam and token IDs
                    beam_id = beam_token_id // vocab_size
                    token_id = beam_token_id % vocab_size

                    effective_beam_id = batch_idx * num_beams + beam_id
                    # add to generated hypotheses if end of sentence
                    if (eos_token_id is not None) and (token_id.item() == eos_token_id):
                        # if beam_token does not belong to top num_beams tokens, it should not be added
                        is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
                        if is_beam_token_worse_than_top_num_beams:
                            continue
                        generated_hyps[batch_idx].add(
                            input_ids[effective_beam_id].clone(),
                            beam_token_score.item(),
                        )
                    else:
                        # add next predicted token since it is not eos_token
                        next_sent_beam.append((beam_token_score, token_id, effective_beam_id))

                    # once the beam for next step is full, don't add more tokens to it.
                    if len(next_sent_beam) == num_beams:
                        break

                # Check if we are done so that we can save a pad step if all(done)
                done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
                    next_scores[batch_idx].max().item(), cur_len
                )

                # update next beam content
                assert len(next_sent_beam) == num_beams, "Beam should always be full"
                next_batch_beam.extend(next_sent_beam)
                assert len(next_batch_beam) == num_beams * (batch_idx + 1), "We should have added num_beams each step"

            # stop when we are done with each sentence
            if all(done):
                break

            # sanity check / prepare next batch
            assert len(next_batch_beam) == batch_size * num_beams
            beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
            beam_tokens = input_ids.new([x[1] for x in next_batch_beam])
            beam_idx = input_ids.new([x[2] for x in next_batch_beam])

            # re-order batch and update current length
            input_ids = input_ids[beam_idx, :]
            input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1)
            cur_len = cur_len + 1

            # re-order internal states
            if past is not None:
                past = self._reorder_cache(past, beam_idx)

            # extend attention_mask for new generated input if only decoder
            # (huxu): move out since we trim attention_mask by ourselves.
            # if self.config.is_encoder_decoder is False:
            #    attention_mask = torch.cat(
            #        [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
            #    )

        # finalize all open beam hypotheses and add to generated hypotheses
        for batch_idx in range(batch_size):
            if done[batch_idx]:
                continue

            # test that beam scores match previously calculated scores if not eos and batch_idx not done
            if eos_token_id is not None and all(
                (token_id % vocab_size).item() != eos_token_id for token_id in next_tokens[batch_idx]
            ):
                assert torch.all(
                    next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx]
                ), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format(
                    next_scores[:, :num_beams][batch_idx],
                    beam_scores.view(batch_size, num_beams)[batch_idx],
                )

            # need to add best num_beams hypotheses to generated hyps
            for beam_id in range(num_beams):
                effective_beam_id = batch_idx * num_beams + beam_id
                final_score = beam_scores[effective_beam_id].item()
                final_tokens = input_ids[effective_beam_id]
                generated_hyps[batch_idx].add(final_tokens, final_score)

        # depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch
        output_batch_size = batch_size if do_sample else batch_size * num_return_sequences
        output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences

        # select the best hypotheses
        sent_lengths = input_ids.new(output_batch_size)
        best = []

        # retrieve best hypotheses
        for i, hypotheses in enumerate(generated_hyps):
            sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0])
            for j in range(output_num_return_sequences_per_batch):
                effective_batch_idx = output_num_return_sequences_per_batch * i + j
                best_hyp = sorted_hyps.pop()[1]
                sent_lengths[effective_batch_idx] = len(best_hyp)
                best.append(best_hyp)

        # prepare for adding eos
        sent_max_len = min(sent_lengths.max().item() + 1, max_length)
        decoded = input_ids.new(output_batch_size, sent_max_len)
        # shorter batches are padded if needed
        if sent_lengths.min().item() != sent_lengths.max().item():
            assert pad_token_id is not None, "`pad_token_id` has to be defined"
            decoded.fill_(pad_token_id)

        # fill with hypotheses and eos_token_id if the latter fits in
        for i, hypo in enumerate(best):
            decoded[i, : sent_lengths[i]] = hypo
            if sent_lengths[i] < max_length:
                decoded[i, sent_lengths[i]] = eos_token_id

        return decoded

    def _generate_no_beam_search(
        self,
        input_ids,
        cur_len,
        max_length,
        min_length,
        do_sample,
        temperature,
        top_k,
        top_p,
        repetition_penalty,
        no_repeat_ngram_size,
        bad_words_ids,
        pad_token_id,
        eos_token_id,
        batch_size,
        attention_mask,
        use_cache,
        model_kwargs,
    ):
        """Generate sequences for each example without beam search (num_beams == 1).
        All returned sequence are generated independantly.
        """
        # length of generated sentences / unfinished sentences
        unfinished_sents = input_ids.new(batch_size).fill_(1)
        sent_lengths = input_ids.new(batch_size).fill_(max_length)

        past = None
        while cur_len < max_length:
            model_inputs = self.prepare_inputs_for_generation(
                input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_kwargs
            )

            outputs = self(**model_inputs, return_dict=True)
            next_token_logits = outputs.logits[:, -1, :]
            scores = self.postprocess_next_token_scores(
                scores=next_token_logits,
                input_ids=input_ids,
                no_repeat_ngram_size=no_repeat_ngram_size,
                bad_words_ids=bad_words_ids,
                cur_len=cur_len,
                min_length=min_length,
                max_length=max_length,
                eos_token_id=eos_token_id,
                repetition_penalty=repetition_penalty,
                batch_size=batch_size,
                num_beams=1,
            )

            # if model has past, then set the past variable to speed up decoding
            if "past_key_values" in outputs:
                past = outputs.past_key_values
            elif "mems" in outputs:
                past = outputs.mems

            if do_sample:
                # Temperature (higher temperature => more likely to sample low probability tokens)
                if temperature != 1.0:
                    scores = scores / temperature
                # Top-p/top-k filtering
                next_token_logscores = top_k_top_p_filtering(scores, top_k=top_k, top_p=top_p)
                # Sample
                probs = F.softmax(next_token_logscores, dim=-1)
                next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
            else:
                # Greedy decoding
                next_token = torch.argmax(next_token_logits, dim=-1)
            
                # print(next_token_logits[0,next_token[0]], next_token_logits[0,eos_token_id])

            # update generations and finished sentences
            if eos_token_id is not None:
                # pad finished sentences if eos_token_id exist
                tokens_to_add = next_token * unfinished_sents + (pad_token_id) * (1 - unfinished_sents)
            else:
                tokens_to_add = next_token

            # add token and increase length by one
            input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1)
            cur_len = cur_len + 1

            if eos_token_id is not None:
                eos_in_sents = tokens_to_add == eos_token_id
                # if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length
                is_sents_unfinished_and_token_to_add_is_eos = unfinished_sents.mul(eos_in_sents.long()).bool()
                sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len)
                # unfinished_sents is set to zero if eos in sentence
                unfinished_sents.mul_((~eos_in_sents).long())

            # stop when there is a </s> in each sentence, or if we exceed the maximul length
            if unfinished_sents.max() == 0:
                break
            
            
            # extend attention_mask for new generated input if only decoder
            # if self.config.is_encoder_decoder is False:
            #     attention_mask = torch.cat(
            #         [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
            #     )

        return input_ids