utils.py

import torch.nn as nn
from transformers.models.gpt2.modeling_gpt2 import GPT2Attention, GPT2MLP
from typing import Optional, Tuple, Union, Any, Dict, List
from transformers import Seq2SeqTrainer, GPT2LMHeadModel
from torch.utils.data.distributed import DistributedSampler
import torch
from transformers.deepspeed import is_deepspeed_zero3_enabled
from transformers.generation.logits_process import LogitsProcessorList
from transformers.generation.stopping_criteria import StoppingCriteriaList
from transformers.generation.utils import GreedySearchOutput, GreedySearchEncoderDecoderOutput, BeamSearchOutput, BeamSearchEncoderDecoderOutput
from transformers.generation.beam_search import BeamScorer

try:
    from torch_geometric.loader import DataLoader
    from torch_geometric.data import Dataset
except ImportError:  
    raise Exception('You need to install torch geometric and its dependecies to use this model please refer to https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html')

class _GPT2LMHeadModel(GPT2LMHeadModel):
    def _init_(self, config):
        super(GPT2LMHeadModel, self).init_(config)
        self.config = config
    
    
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, encoder_outputs=None, **kwargs):
        '''
        This function is an edited version of the prepare_inputs_for_generation function from HuggingFace's transformers 
        https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py
        '''
        token_type_ids = kwargs.get("token_type_ids", None)
        # only last token for inputs_ids if past is defined in kwargs
        if past_key_values:
            input_ids = input_ids[:, -1].unsqueeze(-1)
            if token_type_ids is not None:
                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

        attention_mask = kwargs.get("attention_mask", None)
        position_ids = kwargs.get("position_ids", None)
        if self.config.prot2text_version=="1.1" or self.config.prot2text_version=="1.2":
            encoder_attention_mask = kwargs.get("encoder_attention_mask", None)
        elif self.config.prot2text_version=="1.0":
            encoder_attention_mask = None

        if attention_mask is not None and position_ids is None:
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -1].unsqueeze(-1)
        else:
            position_ids = None

        model_specific_kwargs = {
            "encoder_hidden_states": encoder_outputs['hidden_states'],
        }
        
        return {
            "input_ids": input_ids,
            "past_key_values": past_key_values,
            "use_cache": kwargs.get("use_cache"),
            "position_ids": position_ids,
            "attention_mask": attention_mask,
            "token_type_ids": token_type_ids,
            "encoder_attention_mask": encoder_attention_mask,
            **model_specific_kwargs
        }
    
     
    def greedy_search(
        self,
        input_ids: torch.LongTensor,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[Union[int, List[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,
        synced_gpus: bool = False,
        streamer: Optional["BaseStreamer"] = None,
        **model_kwargs,
    ) -> Union[GreedySearchOutput, torch.LongTensor]:
        '''
        This function is an edited version of the greedy_search function from HuggingFace's transformers 
        https://github.com/huggingface/transformers/blob/main/src/transformers/generation/utils.py
        '''
        
        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
        if max_length is not None:
            warnings.warn(
                "`max_length` is deprecated in this function, use"
                " `stopping_criteria=StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])` instead.",
                UserWarning,
            )
            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
        if isinstance(eos_token_id, int):
            eos_token_id = [eos_token_id]
        eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
        output_attentions = (
            output_attentions if output_attentions is not None else self.generation_config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate
            if return_dict_in_generate is not None
            else self.generation_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
        cross_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
            )

        # keep track of which sequences are already finished
        unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)

        this_peer_finished = False  # used by synced_gpus only
        while True:
            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            # prepare model inputs
            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)

            # forward pass to get next token
            outputs = self(
                **model_inputs,
                return_dict=True,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

            if synced_gpus and this_peer_finished:
                continue  # don't waste resources running the code we don't need

            next_token_logits = outputs.logits[:, -1, :]

            # pre-process distribution
            next_tokens_scores = logits_processor(input_ids, next_token_logits)

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_tokens_scores,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if not self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_attentions,)

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

            # argmax
            next_tokens = torch.argmax(next_tokens_scores, dim=-1)

            # finished sentences should have their next token be a padding token
            if eos_token_id is not None:
                if pad_token_id is None:
                    raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)

            # update generated ids, model inputs, and length for next step
            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
            if streamer is not None:
                streamer.put(next_tokens.cpu())
            model_kwargs = self._update_model_kwargs_for_generation(
                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
            )

            # if eos_token was found in one sentence, set sentence to finished
            if eos_token_id_tensor is not None:
                unfinished_sequences = unfinished_sequences.mul(
                    next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
                )

                # stop when each sentence is finished
                if unfinished_sequences.max() == 0:
                    this_peer_finished = True

            # stop if we exceed the maximum length
            try:
                if stopping_criteria(input_ids, scores):
                    this_peer_finished = True
            except:
                if all(stopping_criteria(input_ids, scores)):
                    this_peer_finished = True

            if this_peer_finished and not synced_gpus:
                break

        if streamer is not None:
            streamer.end()

        if return_dict_in_generate:
            if self.config.is_encoder_decoder:
                return GreedySearchEncoderDecoderOutput(
                    sequences=input_ids,
                    scores=scores,
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return GreedySearchDecoderOnlyOutput(
                    sequences=input_ids,
                    scores=scores,
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return input_ids
        
    def _greedy_search(
        self,
        input_ids: torch.LongTensor,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[Union[int, List[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,
        synced_gpus: bool = False,
        streamer: Optional["BaseStreamer"] = None,
        **model_kwargs,
    ) -> Union[GreedySearchOutput, torch.LongTensor]:
        
        return self.greedy_search(
                            input_ids,
                            logits_processor,
                            stopping_criteria,
                            max_length,
                            pad_token_id,
                            eos_token_id,
                            output_attentions,
                            output_hidden_states,
                            output_scores,
                            return_dict_in_generate,
                            synced_gpus,
                            streamer,
                            **model_kwargs,
                            )
    def _beam_search(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: BeamScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[Union[int, List[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,
        synced_gpus: bool = False,
        **model_kwargs,
    ) -> Union[BeamSearchOutput, torch.LongTensor]:
        
        return self.beam_search(
                input_ids,
                beam_scorer,
                logits_processor,
                stopping_criteria,
                max_length,
                pad_token_id,
                eos_token_id,
                output_attentions,
                output_hidden_states,
                output_scores,
                return_dict_in_generate,
                synced_gpus,
                **model_kwargs,
            )
        
    def beam_search(
        self,
        input_ids: torch.LongTensor,
        beam_scorer: BeamScorer,
        logits_processor: Optional[LogitsProcessorList] = None,
        stopping_criteria: Optional[StoppingCriteriaList] = None,
        max_length: Optional[int] = None,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[Union[int, List[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,
        synced_gpus: bool = False,
        **model_kwargs,
    ) -> Union[BeamSearchOutput, torch.LongTensor]:
        '''
        This function is an edited version of the beam_search function from HuggingFace's transformers 
        https://github.com/huggingface/transformers/blob/main/src/transformers/generation/utils.py
        '''
        # init values
        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
        if max_length is not None:
            warnings.warn(
                "`max_length` is deprecated in this function, use"
                " `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
                UserWarning,
            )
            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
        if len(stopping_criteria) == 0:
            warnings.warn("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
        if isinstance(eos_token_id, int):
            eos_token_id = [eos_token_id]
        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
        output_attentions = (
            output_attentions if output_attentions is not None else self.generation_config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
        )
        return_dict_in_generate = (
            return_dict_in_generate
            if return_dict_in_generate is not None
            else self.generation_config.return_dict_in_generate
        )

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

        batch_beam_size, cur_len = input_ids.shape

        if num_beams * batch_size != batch_beam_size:
            raise ValueError(
                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
            )

        # init attention / hidden states / scores tuples
        scores = () if (return_dict_in_generate and output_scores) else None
        beam_indices = (
            tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
        )
        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
        cross_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
            )

        # initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
        # of the first beam are considered to avoid sampling the exact same tokens across all beams.
        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,))

        this_peer_finished = False  # used by synced_gpus only
        while True:
            if synced_gpus:
                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
                # The following logic allows an early break if all peers finished generating their sequence
                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
                # send 0.0 if we finished, 1.0 otherwise
                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
                # did all peers finish? the reduced sum will be 0.0 then
                if this_peer_finished_flag.item() == 0.0:
                    break

            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,
            )

            if synced_gpus and this_peer_finished:
                cur_len = cur_len + 1
                continue  # don't waste resources running the code we don't need

            next_token_logits = outputs.logits[:, -1, :]
            # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
            # cannot be generated both before and after the `nn.functional.log_softmax` operation.
            # next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
            next_token_scores = nn.functional.log_softmax(
                next_token_logits, dim=-1
            )  # (batch_size * num_beams, vocab_size)

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

            # Store scores, attentions and hidden_states when required
            if return_dict_in_generate:
                if output_scores:
                    scores += (next_token_scores_processed,)
                if output_attentions:
                    decoder_attentions += (
                        (outputs.decoder_attentions,) if not self.config.is_encoder_decoder else (outputs.attentions,)
                    )
                    if self.config.is_encoder_decoder:
                        cross_attentions += (outputs.cross_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)

                

            # Sample 2 next tokens for each beam (so we have some spare tokens and match output of beam search)
            next_token_scores, next_tokens = torch.topk(
                next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True
            )

            next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
            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_indices=beam_indices,
            )

            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)

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

            if return_dict_in_generate and output_scores:
                beam_indices = tuple((beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices))))

            # increase cur_len
            cur_len = cur_len + 1

            try:
                if beam_scorer.is_done or stopping_criteria(input_ids, scores):
                    if not synced_gpus:
                        break
                    else:
                        this_peer_finished = True
            except:
                if beam_scorer.is_done or all(stopping_criteria(input_ids, scores)):
                    if not synced_gpus:
                        break
                    else:
                        this_peer_finished = True
                

        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=stopping_criteria.max_length,
            beam_indices=beam_indices,
        )

        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,
                    beam_indices=sequence_outputs["beam_indices"],
                    encoder_attentions=encoder_attentions,
                    encoder_hidden_states=encoder_hidden_states,
                    decoder_attentions=decoder_attentions,
                    cross_attentions=cross_attentions,
                    decoder_hidden_states=decoder_hidden_states,
                )
            else:
                return BeamSearchDecoderOnlyOutput(
                    sequences=sequence_outputs["sequences"],
                    sequences_scores=sequence_outputs["sequence_scores"],
                    scores=scores,
                    beam_indices=sequence_outputs["beam_indices"],
                    attentions=decoder_attentions,
                    hidden_states=decoder_hidden_states,
                )
        else:
            return sequence_outputs["sequences"]
    

class CABlock(nn.Module):
    '''
        This function is an edited version of the gpt2 decoder block function from HuggingFace's transformers 
        https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py
        '''
    def __init__(self, config, layer_idx=None):
        super().__init__()
        hidden_size = config.hidden_size
        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

        self.crossattention = GPT2Attention(config, is_cross_attention=True, layer_idx=layer_idx)
        self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)

        self.mlp = GPT2MLP(inner_dim, config)

    def forward(
        self,
        hidden_states: Optional[Tuple[torch.FloatTensor]],
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
        

        residual = hidden_states
        hidden_states = self.ln_cross_attn(hidden_states)
        cross_attn_outputs = self.crossattention(
            hidden_states,
            attention_mask=attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
        )
        attn_output = cross_attn_outputs[0]
        # residual connection
        hidden_states = residual + attn_output

        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        # residual connection
        hidden_states = residual + feed_forward_hidden_states

        return (hidden_states,)
    
class Prot2TextTrainer(Seq2SeqTrainer):
    '''
    This function is an edited version of the Seq2SeqTrainer from HuggingFace's transformers 
    '''
    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
        if self.args.world_size > 1:
            eval_sampler = DistributedSampler(self.eval_dataset, num_replicas=self.args.world_size, rank=self.args.process_index)
        else:
            eval_sampler = None
        return DataLoader(
            self.eval_dataset,
            batch_size=self.args.eval_batch_size,
            collate_fn=None,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
            sampler=eval_sampler,
        )
    def get_train_dataloader(self) -> DataLoader:
        if self.args.world_size > 1:
            train_sampler = DistributedSampler(self.train_dataset, num_replicas=self.args.world_size, rank=self.args.process_index)
        else:
            train_sampler = None
        return DataLoader(
            self.train_dataset,
            batch_size=self.args.per_device_train_batch_size,
            collate_fn=None,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
            sampler=train_sampler,
        )
    def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
        """
        Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and
        handling potential state.
        """
        inputs = self._prepare_input(inputs)
        if len(inputs) == 0:
            raise ValueError(
                "The batch received was empty, your model won't be able to train on it. Double-check that your "
                f"training dataset contains keys expected by the model: {','.join(self._signature_columns)}."
            )
        if self.args.past_index >= 0 and self._past is not None:
            inputs["mems"] = self._past
        
        inputs = inputs.to_dict()
        inputs['edge_type'] =  torch.cat([torch.tensor(inputs['edge_type'][i]) for i in range(len(inputs['edge_type']))], dim=0)
        inputs['edge_type'] = torch.argmax(inputs['edge_type'], dim=1)
        inputs = {k: v.to(device=self.args.device, non_blocking=True) if hasattr(v, 'to') else v for k, v in inputs.items()}
        return inputs
    
    def prediction_step(
        self,
        model: nn.Module,
        inputs: Dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[List[str]] = None,
        ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform an evaluation step on `model` using `inputs`.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to evaluate.
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument `labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (`bool`):
                Whether or not to return the loss only.

        Return:
            Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and
            labels (each being optional).
        """

        if not self.args.predict_with_generate or prediction_loss_only:
            return super().prediction_step(
                model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
            )

        has_labels = "labels" in inputs
        inputs = self._prepare_inputs(inputs)

        # XXX: adapt synced_gpus for fairscale as well
        gen_kwargs = self._gen_kwargs.copy()
        if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None:
            gen_kwargs["max_length"] = self.model.config.max_length
        gen_kwargs["num_beams"] = (
            gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.model.config.num_beams
        )
        default_synced_gpus = True if is_deepspeed_zero3_enabled() else False
        gen_kwargs["synced_gpus"] = (
            gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus") is not None else default_synced_gpus
        )

        if "attention_mask" in inputs:
            gen_kwargs["attention_mask"] = inputs.get("attention_mask", None)
        if "global_attention_mask" in inputs:
            gen_kwargs["global_attention_mask"] = inputs.get("global_attention_mask", None)

        generation_inputs = None
        gen_kwargs['x'] = inputs.get('x', None)
        gen_kwargs['edge_index'] = inputs.get('edge_index', None)
        gen_kwargs['edge_type'] = inputs.get('edge_type', None)
        gen_kwargs['batch'] = inputs.get('batch', None)
        gen_kwargs['encoder_input_ids'] = inputs.get('encoder_input_ids', None)
        gen_kwargs['decoder_input_ids'] = inputs.get('decoder_input_ids', None)[:,0:1]
        gen_kwargs["decoder_attention_mask"] = torch.ones(gen_kwargs['decoder_input_ids'].shape[0], 1).to(self.args.device)

        generated_tokens = self.model.generate(
            generation_inputs,
            **gen_kwargs,
        )
        # in case the batch is shorter than max length, the output should be padded
        if gen_kwargs.get("max_length") is not None and generated_tokens.shape[-1] < gen_kwargs["max_length"]:
            generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])
        elif gen_kwargs.get("max_new_tokens") is not None and generated_tokens.shape[-1] < (
            gen_kwargs["max_new_tokens"] + 1
        ):
            generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_new_tokens"] + 1)

        with torch.no_grad():
            if has_labels:
                with self.compute_loss_context_manager():
                    outputs = model(**inputs)
                if self.label_smoother is not None:
                    loss = self.label_smoother(outputs, inputs["labels"]).mean().detach()
                else:
                    loss = (outputs["loss"] if isinstance(outputs, dict) else outputs[0]).mean().detach()
            else:
                loss = None

        if self.args.prediction_loss_only:
            return (loss, None, None)

        if has_labels:
            labels = inputs["labels"]
            if gen_kwargs.get("max_length") is not None and labels.shape[-1] < gen_kwargs["max_length"]:
                labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])
            elif gen_kwargs.get("max_new_tokens") is not None and labels.shape[-1] < (
                gen_kwargs["max_new_tokens"] + 1
            ):
                labels = self._pad_tensors_to_max_len(labels, (gen_kwargs["max_new_tokens"] + 1))
        else:
            labels = None

        return (loss, generated_tokens, labels)