modeling_t5.py

# PyTorch T5 model
# This code is modified based on modeling_t5.py in Huggingface Transformers.


import copy
import logging
import math
import os
import warnings
from typing import Dict, List, Optional, Tuple, Iterable

import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import CrossEntropyLoss

from transformers.configuration_t5 import T5Config
from transformers.file_utils import DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_callable
from transformers.modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from torch_scatter import scatter_mean
from ot import optimal_transport_dist


logger = logging.getLogger(__name__)


####################################################
# This is a conversion method from TF 1.0 to PyTorch
# More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28
####################################################
def load_tf_weights_in_t5(model, config, tf_checkpoint_path):
    """ Load tf checkpoints in a pytorch model.
    """
    try:
        import re
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error(
            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    tf_weights = {}
    for name, shape in init_vars:
        logger.info("Loading TF weight {} with shape {}".format(name, shape))
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        tf_weights[name] = array

    for txt_name in names:
        name = txt_name.split("/")
        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if any(
            n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
            for n in name
        ):
            logger.info("Skipping {}".format("/".join(name)))
            tf_weights.pop(txt_name, None)
            continue
        if "_slot_" in name[-1]:
            logger.info("Skipping {}".format("/".join(name)))
            tf_weights.pop(txt_name, None)
            continue
        pointer = model
        array = tf_weights[txt_name]
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                scope_names = re.split(r"_(\d+)", m_name)
            else:
                scope_names = [m_name]
            if scope_names[0] in ["kernel", "scale", "embedding"]:
                pointer = getattr(pointer, "weight")
            # elif scope_names[0] == 'scale':
            #     pointer = getattr(pointer, 'weight')
            # elif scope_names[0] == 'output_bias' or scope_names[0] == 'beta':
            #     pointer = getattr(pointer, 'bias')
            # elif scope_names[0] == 'squad':
            #     pointer = getattr(pointer, 'classifier')
            else:
                try:
                    pointer = getattr(pointer, scope_names[0])
                except AttributeError:
                    logger.info("Skipping {}".format("/".join(name)))
                    continue
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]
        if scope_names[0] not in ["kernel", "scale", "embedding"]:
            pointer = getattr(pointer, "weight")
        if scope_names[0] != "embedding":
            logger.info("Transposing numpy weight of shape {} for {}".format(array.shape, name))
            array = np.transpose(array)
        try:
            assert pointer.shape == array.shape
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        logger.info("Initialize PyTorch weight {}".format(name))
        pointer.data = torch.from_numpy(array.astype(np.float32))
        tf_weights.pop(txt_name, None)

    logger.info("Weights not copied to PyTorch model: {}".format(", ".join(tf_weights.keys())))
    # logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
    return model


####################################################
# PyTorch Models are constructed by sub-classing
# - torch.nn.Module for the layers and
# - PreTrainedModel for the models (it-self a sub-class of torch.nn.Module)
####################################################

def invert_mask(attention_mask):
    """Turns 1->0, 0->1, False->True, True-> False"""
    assert attention_mask.dim() == 2
    return attention_mask.eq(0)


class T5LayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """ Construct a layernorm module in the T5 style
            No bias and no substraction of mean.
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, x):
        # layer norm should always be calculated in float32
        variance = x.to(torch.float32).pow(2).mean(-1, keepdim=True)
        x = x / torch.sqrt(variance + self.variance_epsilon)

        if self.weight.dtype == torch.float16:
            x = x.to(torch.float16)
        return self.weight * x


class T5DenseReluDense(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(self, hidden_states):
        h = self.wi(hidden_states)
        h = F.relu(h)
        h = self.dropout(h)
        h = self.wo(h)
        return h


class T5LayerFF(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.DenseReluDense = T5DenseReluDense(config)
        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(self, hidden_states):
        norm_x = self.layer_norm(hidden_states)
        y = self.DenseReluDense(norm_x)
        layer_output = hidden_states + self.dropout(y)
        return layer_output


class T5Attention(nn.Module):
    def __init__(self, config: T5Config, has_relative_attention_bias=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.has_relative_attention_bias = has_relative_attention_bias

        self.relative_attention_num_buckets = config.relative_attention_num_buckets
        self.d_model = config.d_model
        self.d_kv = config.d_kv
        self.n_heads = config.num_heads
        self.dropout = config.dropout_rate
        self.inner_dim = self.n_heads * self.d_kv

        # Mesh TensorFlow initialization to avoid scaling before softmax
        self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)

        if self.has_relative_attention_bias:
            self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(heads, self.n_heads, self.d_kv, self.pruned_heads)
        # Prune linear layers
        self.q = prune_linear_layer(self.q, index)
        self.k = prune_linear_layer(self.k, index)
        self.v = prune_linear_layer(self.v, index)
        self.o = prune_linear_layer(self.o, index, dim=1)
        # Update hyper params
        self.n_heads = self.n_heads - len(heads)
        self.inner_dim = self.d_kv * self.n_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    @staticmethod
    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
        """
        Adapted from Mesh Tensorflow:
        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593

        Translate relative position to a bucket number for relative attention.
        The relative position is defined as memory_position - query_position, i.e.
        the distance in tokens from the attending position to the attended-to
        position.  If bidirectional=False, then positive relative positions are
        invalid.
        We use smaller buckets for small absolute relative_position and larger buckets
        for larger absolute relative_positions.  All relative positions >=max_distance
        map to the same bucket.  All relative positions <=-max_distance map to the
        same bucket.  This should allow for more graceful generalization to longer
        sequences than the model has been trained on.
        Args:
            relative_position: an int32 Tensor
            bidirectional: a boolean - whether the attention is bidirectional
            num_buckets: an integer
            max_distance: an integer
        Returns:
            a Tensor with the same shape as relative_position, containing int32
            values in the range [0, num_buckets)
        """
        ret = 0
        n = -relative_position
        if bidirectional:
            num_buckets //= 2
            ret += (n < 0).to(torch.long) * num_buckets  # mtf.to_int32(mtf.less(n, 0)) * num_buckets
            n = torch.abs(n)
        else:
            n = torch.max(n, torch.zeros_like(n))
        # now n is in the range [0, inf)

        # half of the buckets are for exact increments in positions
        max_exact = num_buckets // 2
        is_small = n < max_exact

        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
        val_if_large = max_exact + (
            torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
        ).to(torch.long)
        val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))

        ret += torch.where(is_small, n, val_if_large)
        return ret

    def compute_bias(self, qlen, klen):
        """ Compute binned relative position bias """
        context_position = torch.arange(qlen, dtype=torch.long)[:, None]
        memory_position = torch.arange(klen, dtype=torch.long)[None, :]
        relative_position = memory_position - context_position  # shape (qlen, klen)
        rp_bucket = self._relative_position_bucket(
            relative_position,  # shape (qlen, klen)
            bidirectional=not self.is_decoder,
            num_buckets=self.relative_attention_num_buckets,
        )
        rp_bucket = rp_bucket.to(self.relative_attention_bias.weight.device)
        values = self.relative_attention_bias(rp_bucket)  # shape (qlen, klen, num_heads)
        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, qlen, klen)
        return values

    def forward(
        self,
        input,
        mask=None,
        kv=None,
        position_bias=None,
        past_key_value_state=None,
        head_mask=None,
        query_length=None,
        use_cache=False,
        output_attentions=False,
    ):
        """
        Self-attention (if kv is None) or attention over source sentence (provided by kv).
        """
        # Input is (bs, qlen, dim)
        # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
        # past_key_value_state[0] is (bs, n_heads, q_len - 1, dim_per_head)
        bs, qlen, dim = input.size()

        if past_key_value_state is not None:
            assert self.is_decoder is True, "Encoder cannot cache past key value states"
            assert (
                len(past_key_value_state) == 2
            ), "past_key_value_state should have 2 past states: keys and values. Got {} past states".format(
                len(past_key_value_state)
            )
            real_qlen = qlen + past_key_value_state[0].shape[2] if query_length is None else query_length
        else:
            real_qlen = qlen

        if kv is None:
            klen = real_qlen
        else:
            klen = kv.size(1)

        def shape(x):
            """  projection """
            return x.view(bs, -1, self.n_heads, self.d_kv).transpose(1, 2)

        def unshape(x):
            """  compute context """
            return x.transpose(1, 2).contiguous().view(bs, -1, self.inner_dim)

        q = shape(self.q(input))  # (bs, n_heads, qlen, dim_per_head)

        if kv is None:
            k = shape(self.k(input))  # (bs, n_heads, qlen, dim_per_head)
            v = shape(self.v(input))  # (bs, n_heads, qlen, dim_per_head)
        elif past_key_value_state is None:
            k = v = kv
            k = shape(self.k(k))  # (bs, n_heads, qlen, dim_per_head)
            v = shape(self.v(v))  # (bs, n_heads, qlen, dim_per_head)

        if past_key_value_state is not None:
            if kv is None:
                k_, v_ = past_key_value_state
                k = torch.cat([k_, k], dim=2)  # (bs, n_heads, klen, dim_per_head)
                v = torch.cat([v_, v], dim=2)  # (bs, n_heads, klen, dim_per_head)
            else:
                k, v = past_key_value_state

        if self.is_decoder and use_cache is True:
            present_key_value_state = ((k, v),)
        else:
            present_key_value_state = (None,)

        scores = torch.einsum("bnqd,bnkd->bnqk", q, k)  # (bs, n_heads, qlen, klen)

        if position_bias is None:
            if not self.has_relative_attention_bias:
                raise ValueError("No position_bias provided and no weights to compute position_bias")
            position_bias = self.compute_bias(real_qlen, klen)

            # if key and values are already calculated
            # we want only the last query position bias
            if past_key_value_state is not None:
                position_bias = position_bias[:, :, -1:, :]

            if mask is not None:
                position_bias = position_bias + mask  # (bs, n_heads, qlen, klen)

        scores += position_bias
        weights = F.softmax(scores.float(), dim=-1).type_as(scores)  # (bs, n_heads, qlen, klen)
        weights = F.dropout(weights, p=self.dropout, training=self.training)  # (bs, n_heads, qlen, klen)

        # Mask heads if we want to
        if head_mask is not None:
            weights = weights * head_mask

        context = torch.matmul(weights, v)  # (bs, n_heads, qlen, dim_per_head)
        context = unshape(context)  # (bs, qlen, dim)

        context = self.o(context)

        outputs = (context,) + present_key_value_state

        if output_attentions:
            outputs = outputs + (weights,)
        if self.has_relative_attention_bias:
            outputs = outputs + (position_bias,)
        return outputs


# Structure-aware self-attention
class T5StructAttention(nn.Module):
    def __init__(self, config: T5Config, has_relative_attention_bias=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.has_relative_attention_bias = has_relative_attention_bias

        self.relative_attention_num_buckets = config.relative_attention_num_buckets
        self.d_model = config.d_model
        self.d_kv = config.d_kv
        self.n_heads = config.num_heads
        self.dropout = config.dropout_rate
        self.inner_dim = self.n_heads * self.d_kv

        # Mesh TensorFlow initialization to avoid scaling before softmax
        self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.rel_k = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.rel_v = nn.Linear(self.d_model, self.inner_dim, bias=False)
        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)

        if self.has_relative_attention_bias:
            self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(heads, self.n_heads, self.d_kv, self.pruned_heads)
        # Prune linear layers
        self.q = prune_linear_layer(self.q, index)
        self.k = prune_linear_layer(self.k, index)
        self.v = prune_linear_layer(self.v, index)
        self.rel_k = prune_linear_layer(self.k_rel, index)
        self.rel_v = prune_linear_layer(self.v_rel, index)
        self.o = prune_linear_layer(self.o, index, dim=1)
        # Update hyper params
        self.n_heads = self.n_heads - len(heads)
        self.inner_dim = self.d_kv * self.n_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    @staticmethod
    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
        """
        Adapted from Mesh Tensorflow:
        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593

        Translate relative position to a bucket number for relative attention.
        The relative position is defined as memory_position - query_position, i.e.
        the distance in tokens from the attending position to the attended-to
        position.  If bidirectional=False, then positive relative positions are
        invalid.
        We use smaller buckets for small absolute relative_position and larger buckets
        for larger absolute relative_positions.  All relative positions >=max_distance
        map to the same bucket.  All relative positions <=-max_distance map to the
        same bucket.  This should allow for more graceful generalization to longer
        sequences than the model has been trained on.
        Args:
            relative_position: an int32 Tensor
            bidirectional: a boolean - whether the attention is bidirectional
            num_buckets: an integer
            max_distance: an integer
        Returns:
            a Tensor with the same shape as relative_position, containing int32
            values in the range [0, num_buckets)
        """
        ret = 0
        n = -relative_position
        if bidirectional:
            num_buckets //= 2
            ret += (n < 0).to(torch.long) * num_buckets  # mtf.to_int32(mtf.less(n, 0)) * num_buckets
            n = torch.abs(n)
        else:
            n = torch.max(n, torch.zeros_like(n))
        # now n is in the range [0, inf)

        # half of the buckets are for exact increments in positions
        max_exact = num_buckets // 2
        is_small = n < max_exact

        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
        val_if_large = max_exact + (
            torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
        ).to(torch.long)
        val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))

        ret += torch.where(is_small, n, val_if_large)
        return ret

    def compute_bias(self, qlen, klen):
        """ Compute binned relative position bias """
        context_position = torch.arange(qlen, dtype=torch.long)[:, None]
        memory_position = torch.arange(klen, dtype=torch.long)[None, :]
        relative_position = memory_position - context_position  # shape (qlen, klen)
        rp_bucket = self._relative_position_bucket(
            relative_position,  # shape (qlen, klen)
            bidirectional=not self.is_decoder,
            num_buckets=self.relative_attention_num_buckets,
        )
        rp_bucket = rp_bucket.to(self.relative_attention_bias.weight.device)
        values = self.relative_attention_bias(rp_bucket)  # shape (qlen, klen, num_heads)
        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, qlen, klen)
        return values

    def forward(
        self,
        input,
        mask=None,
        kv=None,
        position_bias=None,
        past_key_value_state=None,
        head_mask=None,
        query_length=None,
        use_cache=False,
        output_attentions=False,
        adj_matrix_emb=None,
    ):
        """
        Self-attention (if kv is None) or attention over source sentence (provided by kv).
        """
        # Input is (bs, qlen, dim)
        # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
        # past_key_value_state[0] is (bs, n_heads, q_len - 1, dim_per_head)
        bs, qlen, dim = input.size()

        if past_key_value_state is not None:
            assert self.is_decoder is True, "Encoder cannot cache past key value states"
            assert (
                len(past_key_value_state) == 2
            ), "past_key_value_state should have 2 past states: keys and values. Got {} past states".format(
                len(past_key_value_state)
            )
            real_qlen = qlen + past_key_value_state[0].shape[2] if query_length is None else query_length
        else:
            real_qlen = qlen

        if kv is None:
            klen = real_qlen
        else:
            klen = kv.size(1)

        def shape(x):
            """  projection """
            return x.view(bs, -1, self.n_heads, self.d_kv).transpose(1, 2)

        def unshape(x):
            """  compute context """
            return x.transpose(1, 2).contiguous().view(bs, -1, self.inner_dim)

        q = shape(self.q(input))  # (bs, n_heads, qlen, dim_per_head)

        if kv is None:
            k = shape(self.k(input))  # bs * n_heads * klen * dim_per_head
            v = shape(self.v(input))  # bs * n_heads * klen * dim_per_head
        elif past_key_value_state is None:
            k = v = kv
            k = shape(self.k(k))  # bs * n_heads * klen * dim_per_head
            v = shape(self.v(v))  # bs * n_heads * klen * dim_per_head

        rel_k = shape(self.rel_k(adj_matrix_emb)).contiguous().view(bs, self.n_heads, qlen, -1, self.d_kv) # bs * n_heads * qlen * klen * dim_per_head
        rel_v = shape(self.rel_v(adj_matrix_emb)).contiguous().view(bs, self.n_heads, qlen, -1, self.d_kv) # bs * n_heads * qlen * klen * dim_per_head)

        if past_key_value_state is not None:
            if kv is None:
                k_, v_ = past_key_value_state
                k = torch.cat([k_, k], dim=2)  # bs * n_heads * klen * dim_per_head
                v = torch.cat([v_, v], dim=2)  # bs * n_heads * klen * dim_per_head
            else:
                k, v = past_key_value_state

        if self.is_decoder and use_cache is True:
            present_key_value_state = ((k, v),)
        else:
            present_key_value_state = (None,)

        scores = torch.einsum("bnqd,bnkd->bnqk", q, k)  # bs * n_heads * qlen * klen)
        scores_rel = torch.einsum('aebc,aebdc->aebd', q, rel_k)  # bs * n_heads * qlen * klen
        scores = scores + scores_rel

        scores += mask
        weights = F.softmax(scores.float(), dim=-1).type_as(scores)  # bs * n_heads * qlen * klen
        weights = F.dropout(weights, p=self.dropout, training=self.training)  # bs * n_heads * qlen * klen

        # Mask heads if we want to
        if head_mask is not None:
            weights = weights * head_mask

        context = torch.matmul(weights, v)  # bs * n_heads * qlen * dim_per_head

        context_rel = torch.einsum('abcd,abcde->abce', weights, rel_v)  # bs * n_heads * qlen * dim_per_head

        context = context + context_rel

        context = unshape(context)  # bs * qlen * dim

        context = self.o(context)

        outputs = (context,) + present_key_value_state

        if output_attentions:
            outputs = outputs + (weights,)
        if self.has_relative_attention_bias:
            outputs = outputs + (position_bias,)
        return outputs


class T5LayerSelfAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        head_mask=None,
        past_key_value_state=None,
        use_cache=False,
        output_attentions=False,
    ):
        norm_x = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(
            norm_x,
            mask=attention_mask,
            position_bias=position_bias,
            head_mask=head_mask,
            past_key_value_state=past_key_value_state,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        y = attention_output[0]
        layer_output = hidden_states + self.dropout(y)
        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
        return outputs


# Structure-aware semantic aggregation module
class T5StructLayerSelfAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.SelfStructAttention = T5StructAttention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        input_node_ids=None,
        input_edge_ids=None,
        node_attention_mask=None,
        edge_attention_mask=None,
        extended_node_attention_mask=None,
        extended_edge_attention_mask=None,
        adj_matrix=None,
        position_bias=None,
        head_mask=None,
        past_key_value_state=None,
        use_cache=False,
        output_attentions=False,
    ):
        norm_x = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(
            norm_x,
            mask=attention_mask,
            position_bias=position_bias,
            head_mask=head_mask,
            past_key_value_state=past_key_value_state,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        y = attention_output[0]

        # get each representation vector of nodes
        x_ori = y  # batch * len * embed_dim
        x_node = scatter_mean(x_ori, input_node_ids, dim=1)  # batch * node_size * embed_dim
        assert x_node.size(1) == node_attention_mask.size(1) == adj_matrix.size(1)

        # get each representation vector of edges
        x_edge = scatter_mean(x_ori, input_edge_ids, dim=1)  # batch * edge_size * embed_dim
        assert x_edge.size(1) == edge_attention_mask.size(1)
        x_edge = edge_attention_mask.unsqueeze(-1).repeat(1, 1, x_edge.size(2)) * x_edge

        # lookup adj_matrix in edge representation
        node_len, edge_len, emb_dim = adj_matrix.size(1), x_edge.size(1), x_edge.size(2)
        adj_matrix_tmp = adj_matrix.contiguous().view(-1, node_len * node_len).unsqueeze(-1).repeat(1, 1, emb_dim)
        adj_matrix_emb = torch.gather(x_edge, 1, adj_matrix_tmp).view(-1, node_len, node_len,
                                                                      emb_dim)  # batch * node_len * node_len emb_dim
        adj_matrix_emb = adj_matrix_emb.contiguous().view(-1, node_len * node_len, emb_dim)  #  batch * (node_len * node_len) * emb_dim

        struct_attention_output = self.SelfStructAttention(
            x_node, mask=extended_node_attention_mask, position_bias=None,
            head_mask=head_mask, past_key_value_state=past_key_value_state, use_cache=use_cache,
            output_attentions=output_attentions, adj_matrix_emb=adj_matrix_emb,
        )
        x_node = struct_attention_output[0]
        x_node = node_attention_mask.unsqueeze(-1).repeat(1, 1, x_node.size(2)) * x_node

        y = y + torch.gather(x_node, 1, input_node_ids.unsqueeze(-1).repeat(1, 1, x_node.size(2)))

        layer_output = hidden_states + self.dropout(y)
        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
        return outputs


class T5LayerCrossAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.EncDecAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states,
        kv,
        attention_mask=None,
        position_bias=None,
        head_mask=None,
        past_key_value_state=None,
        use_cache=False,
        query_length=None,
        output_attentions=False,
    ):
        norm_x = self.layer_norm(hidden_states)
        attention_output = self.EncDecAttention(
            norm_x,
            mask=attention_mask,
            kv=kv,
            position_bias=position_bias,
            head_mask=head_mask,
            past_key_value_state=past_key_value_state,
            use_cache=use_cache,
            query_length=query_length,
            output_attentions=output_attentions,
        )
        y = attention_output[0]
        layer_output = hidden_states + self.dropout(y)
        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
        return outputs


class T5Block(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.layer = nn.ModuleList()
        self.layer.append(T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
        if self.is_decoder:
            self.layer.append(T5LayerCrossAttention(config, has_relative_attention_bias=has_relative_attention_bias))

        self.layer.append(T5LayerFF(config))

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        encoder_decoder_position_bias=None,
        head_mask=None,
        past_key_value_state=None,
        use_cache=False,
        output_attentions=False,
    ):

        if past_key_value_state is not None:
            assert self.is_decoder, "Only decoder can use `past_key_value_states`"
            expected_num_past_key_value_states = 2 if encoder_hidden_states is None else 4

            error_message = "There should be {} past states. 2 (past / key) for self attention.{} Got {} past key / value states".format(
                expected_num_past_key_value_states,
                "2 (past / key) for cross attention" if expected_num_past_key_value_states == 4 else "",
                len(past_key_value_state),
            )
            assert len(past_key_value_state) == expected_num_past_key_value_states, error_message

            self_attn_past_key_value_state = past_key_value_state[:2]
            cross_attn_past_key_value_state = past_key_value_state[2:]
        else:
            self_attn_past_key_value_state, cross_attn_past_key_value_state = None, None

        self_attention_outputs = self.layer[0](
            hidden_states,
            attention_mask=attention_mask,
            position_bias=position_bias,
            head_mask=head_mask,
            past_key_value_state=self_attn_past_key_value_state,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        hidden_states, present_key_value_state = self_attention_outputs[:2]
        attention_outputs = self_attention_outputs[2:]  # Keep self-attention outputs and relative position weights

        if self.is_decoder and encoder_hidden_states is not None:
            # the actual query length is unknown for cross attention
            # if using past key value states. Need to inject it here
            if present_key_value_state is not None:
                query_length = present_key_value_state[0].shape[2]
            else:
                query_length = None

            cross_attention_outputs = self.layer[1](
                hidden_states,
                kv=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                position_bias=encoder_decoder_position_bias,
                head_mask=head_mask,
                past_key_value_state=cross_attn_past_key_value_state,
                query_length=query_length,
                use_cache=use_cache,
                output_attentions=output_attentions,
            )
            hidden_states = cross_attention_outputs[0]
            # Combine self attn and cross attn key value states
            if present_key_value_state is not None:
                present_key_value_state = present_key_value_state + cross_attention_outputs[1]

            # Keep cross-attention outputs and relative position weights
            attention_outputs = attention_outputs + cross_attention_outputs[2:]

        # Apply Feed Forward layer
        hidden_states = self.layer[-1](hidden_states)
        outputs = (hidden_states,)

        # Add attentions if we output them
        outputs = outputs + (present_key_value_state,) + attention_outputs
        return outputs  # hidden-states, present_key_value_states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)


# T5 block with structure-aware semantic aggregation module
class T5StructBlock(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.is_decoder = config.is_decoder
        self.layer = nn.ModuleList()
        self.layer.append(T5StructLayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
        if self.is_decoder:
            self.layer.append(T5LayerCrossAttention(config, has_relative_attention_bias=has_relative_attention_bias))

        self.layer.append(T5LayerFF(config))

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        input_node_ids=None,
        input_edge_ids=None,
        node_attention_mask=None,
        edge_attention_mask=None,
        extended_node_attention_mask=None,
        extended_edge_attention_mask=None,
        adj_matrix=None,
        position_bias=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        encoder_decoder_position_bias=None,
        head_mask=None,
        past_key_value_state=None,
        use_cache=False,
        output_attentions=False,
    ):

        if past_key_value_state is not None:
            assert self.is_decoder, "Only decoder can use `past_key_value_states`"
            expected_num_past_key_value_states = 2 if encoder_hidden_states is None else 4

            error_message = "There should be {} past states. 2 (past / key) for self attention.{} Got {} past key / value states".format(
                expected_num_past_key_value_states,
                "2 (past / key) for cross attention" if expected_num_past_key_value_states == 4 else "",
                len(past_key_value_state),
            )
            assert len(past_key_value_state) == expected_num_past_key_value_states, error_message

            self_attn_past_key_value_state = past_key_value_state[:2]
            cross_attn_past_key_value_state = past_key_value_state[2:]
        else:
            self_attn_past_key_value_state, cross_attn_past_key_value_state = None, None

        self_attention_outputs = self.layer[0](
            hidden_states,
            attention_mask=attention_mask,
            input_node_ids=input_node_ids,
            input_edge_ids=input_edge_ids,
            node_attention_mask=node_attention_mask,
            edge_attention_mask=edge_attention_mask,
            extended_node_attention_mask=extended_node_attention_mask,
            extended_edge_attention_mask=extended_edge_attention_mask,
            adj_matrix=adj_matrix,
            position_bias=position_bias,
            head_mask=head_mask,
            past_key_value_state=self_attn_past_key_value_state,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        hidden_states, present_key_value_state = self_attention_outputs[:2]
        attention_outputs = self_attention_outputs[2:]  # Keep self-attention outputs and relative position weights

        if self.is_decoder and encoder_hidden_states is not None:
            # the actual query length is unknown for cross attention
            # if using past key value states. Need to inject it here
            if present_key_value_state is not None:
                query_length = present_key_value_state[0].shape[2]
            else:
                query_length = None

            cross_attention_outputs = self.layer[1](
                hidden_states,
                kv=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                position_bias=encoder_decoder_position_bias,
                head_mask=head_mask,
                past_key_value_state=cross_attn_past_key_value_state,
                query_length=query_length,
                use_cache=use_cache,
                output_attentions=output_attentions,
            )
            hidden_states = cross_attention_outputs[0]
            # Combine self attn and cross attn key value states
            if present_key_value_state is not None:
                present_key_value_state = present_key_value_state + cross_attention_outputs[1]

            # Keep cross-attention outputs and relative position weights
            attention_outputs = attention_outputs + cross_attention_outputs[2:]

        # Apply Feed Forward layer
        hidden_states = self.layer[-1](hidden_states)
        outputs = (hidden_states,)

        # Add attentions if we output them
        outputs = outputs + (present_key_value_state,) + attention_outputs
        return outputs  # hidden-states, present_key_value_states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)


class T5PreTrainedModel(PreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for downloading and loading pretrained models.
    """

    config_class = T5Config
    load_tf_weights = load_tf_weights_in_t5
    base_model_prefix = "transformer"

    @property
    def dummy_inputs(self):
        input_ids = torch.tensor(DUMMY_INPUTS)
        input_mask = torch.tensor(DUMMY_MASK)
        dummy_inputs = {
            "decoder_input_ids": input_ids,
            "input_ids": input_ids,
            "decoder_attention_mask": input_mask,
        }
        return dummy_inputs

    def _init_weights(self, module):
        """ Initialize the weights """
        factor = self.config.initializer_factor  # Used for testing weights initialization
        if isinstance(module, T5LayerNorm):
            module.weight.data.fill_(factor * 1.0)
        elif isinstance(module, (T5Model, T5ForConditionalGeneration)):
            # Mesh TensorFlow embeddings initialization
            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
            module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
        elif isinstance(module, T5DenseReluDense):
            # Mesh TensorFlow FF initialization
            # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
            # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
            module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
            if hasattr(module.wi, "bias") and module.wi.bias is not None:
                module.wi.bias.data.zero_()
            module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
            if hasattr(module.wo, "bias") and module.wo.bias is not None:
                module.wo.bias.data.zero_()
        elif isinstance(module, T5Attention):
            # Mesh TensorFlow attention initialization to avoid scaling before softmax
            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
            d_model = self.config.d_model
            d_kv = self.config.d_kv
            n_heads = self.config.num_heads
            module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * d_kv) ** -0.5))
            module.k.weight.data.normal_(mean=0.0, std=factor * (d_model ** -0.5))
            module.v.weight.data.normal_(mean=0.0, std=factor * (d_model ** -0.5))
            module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * d_kv) ** -0.5))
            if module.has_relative_attention_bias:
                module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))

    def _shift_right(self, input_ids):
        decoder_start_token_id = self.config.decoder_start_token_id
        pad_token_id = self.config.pad_token_id

        assert (
            decoder_start_token_id is not None
        ), "self.model.config.decoder_start_token_id has to be defined. In T5 it is usually set to the pad_token_id. See T5 docs for more information"

        # shift inputs to the right
        shifted_input_ids = input_ids.new_zeros(input_ids.shape)
        shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
        shifted_input_ids[..., 0] = decoder_start_token_id

        assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
        # replace possible -100 values in labels by `pad_token_id`
        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

        assert torch.all(shifted_input_ids >= 0).item(), "Verify that `labels` has only positive values and -100"

        return shifted_input_ids


class T5Stack(T5PreTrainedModel):
    def __init__(self, config, embed_tokens=None):
        super().__init__(config)

        self.embed_tokens = embed_tokens
        self.is_decoder = config.is_decoder

        self.block = nn.ModuleList(
            [T5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)]
        )
        self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

        self.init_weights()

    def get_input_embeddings(self):
        return self.embed_tokens

    def get_output_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, new_embeddings):
        self.embed_tokens = new_embeddings

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        inputs_embeds=None,
        head_mask=None,
        past_key_value_states=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
    ):

        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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
        )

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            if self.is_decoder:
                raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
            else:
                raise ValueError("You have to specify either input_ids or inputs_embeds")

        if inputs_embeds is None:
            assert self.embed_tokens is not None, "You have to intialize the model with valid token embeddings"
            inputs_embeds = self.embed_tokens(input_ids)

        batch_size, seq_length = input_shape

        if past_key_value_states is not None:
            assert seq_length == 1, "Input shape is {}, but should be {} when using past_key_value_sates".format(
                input_shape, (batch_size, 1)
            )
            # required mask seq length can be calculated via length of past
            # key value states and seq_length = 1 for the last token
            mask_seq_length = past_key_value_states[0][0].shape[2] + seq_length
        else:
            mask_seq_length = seq_length

        if attention_mask is None:
            attention_mask = torch.ones(batch_size, mask_seq_length).to(inputs_embeds.device)
        if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
            encoder_seq_length = encoder_hidden_states.shape[1]
            encoder_attention_mask = torch.ones(
                batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
            )

        # initialize past_key_value_states with `None` if past does not exist
        if past_key_value_states is None:
            past_key_value_states = [None] * len(self.block)

        # ourselves in which case we just need to make it broadcastable to all heads.
        # after this, zero means attending while -10000.0 means no attending
        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, inputs_embeds.device)

        if self.is_decoder and encoder_attention_mask is not None:
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        head_mask = self.get_head_mask(head_mask, self.config.num_layers)
        present_key_value_states = ()
        all_hidden_states = ()
        all_attentions = ()
        position_bias = None
        encoder_decoder_position_bias = None

        hidden_states = self.dropout(inputs_embeds)

        for i, (layer_module, past_key_value_state) in enumerate(zip(self.block, past_key_value_states)):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_outputs = layer_module(
                hidden_states,
                attention_mask=extended_attention_mask,
                position_bias=position_bias,
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_extended_attention_mask,
                encoder_decoder_position_bias=encoder_decoder_position_bias,
                head_mask=head_mask[i],
                past_key_value_state=past_key_value_state,
                use_cache=use_cache,
                output_attentions=output_attentions,
            )
            # layer_outputs is a tuple with:
            # hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
            hidden_states, present_key_value_state = layer_outputs[:2]

            if i == 0:
                # We share the position biases between the layers - the first layer store them
                # layer_outputs = hidden-states, key-value-states (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
                position_bias = layer_outputs[3 if output_attentions else 2]
                if self.is_decoder and encoder_hidden_states is not None:
                    encoder_decoder_position_bias = layer_outputs[5 if output_attentions else 3]
            # append next layer key value states
            present_key_value_states = present_key_value_states + (present_key_value_state,)

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[2],)  # We keep only self-attention weights for now

        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states)

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        outputs = (hidden_states,)
        if use_cache is True:
            assert self.is_decoder, "`use_cache` can only be set to `True` if {} is used as a decoder".format(self)
            outputs = outputs + (present_key_value_states,)
        if output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if output_attentions:
            outputs = outputs + (all_attentions,)
        return outputs  # last-layer hidden state, (presents,) (all hidden states), (all attentions)


def convert_length_to_mask(len_list, max_length):
    len_size = max_length
    batch_size = len_list.size(0)
    seq_range = torch.arange(0, len_size, device=len_list.device)
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, len_size)
    length_expand = len_list.expand_as(seq_range_expand)
    attention_mask = seq_range_expand < length_expand
    return attention_mask


# T5 stack with structure-aware semantic aggregation module
class T5StructStack(T5PreTrainedModel):
    def __init__(self, config, embed_tokens=None):
        super().__init__(config)

        self.embed_tokens = embed_tokens
        self.is_decoder = config.is_decoder

        self.block = nn.ModuleList(
            [T5StructBlock(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)]
        )
        self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

        self.init_weights()

    def get_input_embeddings(self):
        return self.embed_tokens

    def get_output_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, new_embeddings):
        self.embed_tokens = new_embeddings

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        input_node_ids=None,
        input_edge_ids=None,
        node_length=None,
        edge_length=None,
        adj_matrix=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        inputs_embeds=None,
        head_mask=None,
        past_key_value_states=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
    ):

        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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
        )

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            if self.is_decoder:
                raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
            else:
                raise ValueError("You have to specify either input_ids or inputs_embeds")

        if inputs_embeds is None:
            assert self.embed_tokens is not None, "You have to intialize the model with valid token embeddings"
            inputs_embeds = self.embed_tokens(input_ids)

        batch_size, seq_length = input_shape

        if past_key_value_states is not None:
            assert seq_length == 1, "Input shape is {}, but should be {} when using past_key_value_sates".format(
                input_shape, (batch_size, 1)
            )
            # required mask seq length can be calculated via length of past
            # key value states and seq_length = 1 for the last token
            mask_seq_length = past_key_value_states[0][0].shape[2] + seq_length
        else:
            mask_seq_length = seq_length

        if attention_mask is None:
            attention_mask = torch.ones(batch_size, mask_seq_length).to(inputs_embeds.device)
        if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
            encoder_seq_length = encoder_hidden_states.shape[1]
            encoder_attention_mask = torch.ones(
                batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
            )

        # initialize past_key_value_states with `None` if past does not exist
        if past_key_value_states is None:
            past_key_value_states = [None] * len(self.block)

        # ourselves in which case we just need to make it broadcastable to all heads.
        # after this, zero means attending while -10000.0 means no attending
        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, inputs_embeds.device)

        # build attention mask and invert
        # node/edge_attention_mask: 1 means attending while 0 means no attending (batch, len)
        # extended_node/edge_attention_mask: 0 means attending while -10000.0 means no attending (batch, 1, 1, len)
        node_attention_mask = convert_length_to_mask(node_length, input_node_ids.max()+1)
        edge_attention_mask = convert_length_to_mask(edge_length, input_edge_ids.max()+1)
        extended_node_attention_mask = self.get_extended_attention_mask(node_attention_mask, (batch_size, input_node_ids.max()+1), input_node_ids.device)
        extended_edge_attention_mask = self.get_extended_attention_mask(edge_attention_mask, (batch_size, input_edge_ids.max()+1), input_edge_ids.device)

        if self.is_decoder and encoder_attention_mask is not None:
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        head_mask = self.get_head_mask(head_mask, self.config.num_layers)
        present_key_value_states = ()
        all_hidden_states = ()
        all_attentions = ()
        position_bias = None
        encoder_decoder_position_bias = None

        hidden_states = self.dropout(inputs_embeds)

        for i, (layer_module, past_key_value_state) in enumerate(zip(self.block, past_key_value_states)):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_outputs = layer_module(
                hidden_states,
                attention_mask=extended_attention_mask,
                input_node_ids=input_node_ids,
                input_edge_ids=input_edge_ids,
                node_attention_mask=node_attention_mask,
                edge_attention_mask=edge_attention_mask,
                extended_node_attention_mask=extended_node_attention_mask,
                extended_edge_attention_mask=extended_edge_attention_mask,
                adj_matrix=adj_matrix,
                position_bias=position_bias,
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_extended_attention_mask,
                encoder_decoder_position_bias=encoder_decoder_position_bias,
                head_mask=head_mask[i],
                past_key_value_state=past_key_value_state,
                use_cache=use_cache,
                output_attentions=output_attentions,
            )
            # layer_outputs is a tuple with:
            # hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
            hidden_states, present_key_value_state = layer_outputs[:2]

            if i == 0:
                # We share the position biases between the layers - the first layer store them
                # layer_outputs = hidden-states, key-value-states (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
                position_bias = layer_outputs[3 if output_attentions else 2]
                if self.is_decoder and encoder_hidden_states is not None:
                    encoder_decoder_position_bias = layer_outputs[5 if output_attentions else 3]
            # append next layer key value states
            present_key_value_states = present_key_value_states + (present_key_value_state,)

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[2],)  # We keep only self-attention weights for now

        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states)

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        outputs = (hidden_states,)
        if use_cache is True:
            assert self.is_decoder, "`use_cache` can only be set to `True` if {} is used as a decoder".format(self)
            outputs = outputs + (present_key_value_states,)
        if output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if output_attentions:
            outputs = outputs + (all_attentions,)
        return outputs  # last-layer hidden state, (presents,) (all hidden states), (all attentions)


class T5Model(T5PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.shared = nn.Embedding(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        encoder_config.use_cache = False
        self.encoder = T5Stack(encoder_config, self.shared)

        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        self.decoder = T5Stack(decoder_config, self.shared)

        self.init_weights()

    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
        self.decoder.set_input_embeddings(new_embeddings)

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def _prune_heads(self, heads_to_prune):
        """ Prunes heads of the model.
            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
            See base class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_outputs=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        decoder_past_key_value_states=None,
        use_cache=None,
        inputs_embeds=None,
        decoder_inputs_embeds=None,
        head_mask=None,
        output_attentions=None,
        output_hidden_states=None,
    ):
        r"""
    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs:
        last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
            If `decoder_past_key_value_states` is used only the last hidden-state of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
        decoder_past_key_value_states (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`, `optional`, returned when ``use_cache=True``):
            Contains pre-computed key and value hidden-states of the attention blocks.
            Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input).
            Note that when using `decoder_past_key_value_states`, the model only outputs the last `hidden-state` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`.
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.

        Example::

            >>> from transformers import T5Tokenizer, T5Model

            >>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
            >>> model = T5Model.from_pretrained('t5-small')

            >>> input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="pt")  # Batch size 1
            >>> outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)

            >>> last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
        """
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        # Encode if needed (training, first prediction pass)
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

        hidden_states = encoder_outputs[0]

        # If decoding with past key value states, only the last tokens
        # should be given as an input
        if decoder_past_key_value_states is not None:
            if decoder_input_ids is not None:
                decoder_input_ids = decoder_input_ids[:, -1:]
            if decoder_inputs_embeds is not None:
                decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_value_states=decoder_past_key_value_states,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )

        if use_cache is True:
            past = ((encoder_outputs, decoder_outputs[1]),)
            decoder_outputs = decoder_outputs[:1] + past + decoder_outputs[2:]

        return decoder_outputs + encoder_outputs


class T5ForConditionalGeneration(T5PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.model_dim = config.d_model

        self.shared = nn.Embedding(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        encoder_config.use_cache = False
        self.encoder = T5Stack(encoder_config, self.shared)

        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        self.decoder = T5Stack(decoder_config, self.shared)

        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        self.init_weights()

    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
        self.decoder.set_input_embeddings(new_embeddings)

    def get_output_embeddings(self):
        return self.lm_head

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_outputs=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        decoder_past_key_value_states=None,
        use_cache=None,
        labels=None,
        inputs_embeds=None,
        decoder_inputs_embeds=None,
        head_mask=None,
        output_attentions=None,
        output_hidden_states=None,
        **kwargs
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
            Labels for computing the sequence classification/regression loss.
            Indices should be in :obj:`[-100, 0, ..., config.vocab_size - 1]`.
            All labels set to ``-100`` are ignored (masked), the loss is only
            computed for labels in ``[0, ..., config.vocab_size]``
        kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
            Used to hide legacy arguments that have been deprecated.

    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs:
        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
            Classification loss (cross entropy).
        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
            If `past_key_value_states` is used only the last prediction_scores of the sequences of shape :obj:`(batch_size, 1, hidden_size)` is output.
        decoder_past_key_value_states (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length, embed_size_per_head)`, `optional`, returned when ``use_cache=True``):
            Contains pre-computed key and value hidden-states of the attention blocks.
            Can be used to speed up sequential decoding (see `decoder_past_key_value_states` input).
            Note that when using `decoder_past_key_value_states`, the model only outputs the last `prediction_score` of the sequence of shape :obj:`(batch_size, 1, config.vocab_size)`.
        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
            of shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.

    Examples::

        >>> from transformers import T5Tokenizer, T5ForConditionalGeneration

        >>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
        >>> model = T5ForConditionalGeneration.from_pretrained('t5-small')
        >>> input_ids = tokenizer.encode("Hello, my dog is cute", return_tensors="pt")  # Batch size 1
        >>> outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, labels=input_ids)
        >>> loss, prediction_scores = outputs[:2]

        >>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
        >>> model = T5ForConditionalGeneration.from_pretrained('t5-small')
        >>> input_ids = tokenizer.encode("summarize: Hello, my dog is cute", return_tensors="pt")  # Batch size 1
        >>> outputs = model.generate(input_ids)
        """

        if "lm_labels" in kwargs:
            warnings.warn(
                "The `lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
                DeprecationWarning,
            )
            labels = kwargs.pop("lm_labels")
        assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."

        use_cache = use_cache if use_cache is not None else self.config.use_cache

        # Encode if needed (training, first prediction pass)
        if encoder_outputs is None:
            # Convert encoder inputs in embeddings if needed
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

        hidden_states = encoder_outputs[0]

        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
            # get decoder inputs from shifting lm labels to the right
            decoder_input_ids = self._shift_right(labels)

        # If decoding with past key value states, only the last tokens
        # should be given as an input
        if decoder_past_key_value_states is not None:
            assert labels is None, "Decoder should not use cached key value states when training."
            if decoder_input_ids is not None:
                decoder_input_ids = decoder_input_ids[:, -1:]
            if decoder_inputs_embeds is not None:
                decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_value_states=decoder_past_key_value_states,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )

        # insert decoder past at right place
        # to speed up decoding
        if use_cache is True:
            past = ((encoder_outputs, decoder_outputs[1]),)
            decoder_outputs = decoder_outputs[:1] + past + decoder_outputs[2:]

        sequence_output = decoder_outputs[0]
        # Rescale output before projecting on vocab
        # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
        sequence_output = sequence_output * (self.model_dim ** -0.5)
        lm_logits = self.lm_head(sequence_output)

        decoder_outputs = (lm_logits,) + decoder_outputs[1:]  # Add hidden states and attention if they are here
        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
            # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
            decoder_outputs = (loss,) + decoder_outputs

        return decoder_outputs + encoder_outputs

    def prepare_inputs_for_generation(self, input_ids, past, attention_mask, use_cache, **kwargs):
        assert past is not None, "past has to be defined for encoder_outputs"

        encoder_outputs, decoder_past_key_value_states = past

        return {
            "decoder_input_ids": input_ids,
            "decoder_past_key_value_states": decoder_past_key_value_states,
            "encoder_outputs": encoder_outputs,
            "attention_mask": attention_mask,
            "use_cache": use_cache,
        }

    def _reorder_cache(self, past, beam_idx):
        # if decoder past is not included in output
        # speedy decoding is disabled and no need to reorder
        if past[1] is None:
            logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
            return past

        decoder_past = past[1]
        past = (past[0],)
        reordered_decoder_past = ()
        for layer_past_states in decoder_past:
            # get the correct batch idx from layer past batch dim
            # batch dim of `past` is at 2nd position
            reordered_layer_past_states = ()
            for layer_past_state in layer_past_states:
                # need to set correct `past` for each of the four key / value states
                reordered_layer_past_states = reordered_layer_past_states + (
                    layer_past_state.index_select(0, beam_idx),
                )

            assert reordered_layer_past_states[0].shape == layer_past_states[0].shape
            assert len(reordered_layer_past_states) == len(layer_past_states)

            reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
        return past + (reordered_decoder_past,)


class MyT5Pretrain(T5ForConditionalGeneration):
    def __init__(self, config):
        super().__init__(config)
        self.model_dim = config.d_model

        self.shared = nn.Embedding(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        encoder_config.use_cache = False
        self.encoder = T5StructStack(encoder_config, self.shared)

        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        self.decoder = T5Stack(decoder_config, self.shared)

        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        self.init_weights()

    def forward(self, input_ids=None, attention_mask=None, encoder_outputs=None, encoder_label=None,
        decoder_input_ids=None, decoder_attention_mask=None,input_node_ids = None, input_edge_ids = None,
        node_length=None, edge_length=None, adj_matrix=None, decoder_whole_ids=None, word_length=None,
        decoder_past_key_value_states=None, use_cache=False, inputs_embeds=None, decoder_inputs_embeds=None, head_mask=None,
        output_attentions=None, output_hidden_states=None, is_training=False):

        if is_training:
            _decoder_input_ids = self._shift_right(decoder_input_ids)
        else:
            _decoder_input_ids = decoder_input_ids

        if encoder_outputs is None:
            # Convert encoder inputs in embeddings if needed
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                input_node_ids=input_node_ids,
                input_edge_ids=input_edge_ids,
                node_length=node_length,
                edge_length=edge_length,
                adj_matrix=adj_matrix,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

        hidden_states = encoder_outputs[0]

        # If decoding with past key value states, only the last tokens
        # should be given as an input
        if decoder_past_key_value_states is not None:
            #assert labels is None, "Decoder should not use cached key value states when training."
            assert is_training is False, "Decoder should not use cached key value states when training."
            if _decoder_input_ids is not None:
                _decoder_input_ids = _decoder_input_ids[:, -1:]
            if decoder_inputs_embeds is not None:
                decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]

        # Decode
        decoder_outputs = self.decoder(
            input_ids=_decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_value_states=decoder_past_key_value_states,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )

        # insert decoder past at right place
        # to speed up decoding
        if use_cache is True:
            past = ((encoder_outputs, decoder_outputs[1]),)
            decoder_outputs = decoder_outputs[:1] + past + decoder_outputs[2:]

        sequence_output = decoder_outputs[0]
        # Rescale output before projecting on vocab
        # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
        sequence_output_rescale = sequence_output * (self.model_dim ** -0.5)
        hidden_states_rescale = hidden_states * (self.model_dim ** -0.5)
        lm_logits_decoder = self.lm_head(sequence_output_rescale)
        lm_logits_encoder = self.lm_head(hidden_states_rescale)

        decoder_outputs = (lm_logits_decoder,) + decoder_outputs[1:]  # Add hidden states and attention if they are here
        if encoder_label is None:
            if decoder_whole_ids is None:
                # Task 1: complete graph + corrupted text --> complete text
                if is_training:
                    loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
                    loss = loss_fct(lm_logits_decoder.view(-1, self.config.vocab_size), decoder_input_ids.view(-1))
                    decoder_outputs = (loss,) + decoder_outputs
                    return loss
            else:
                # Task 3: complete graph + complete text --> ot loss
                if is_training:
                    # get each representation vector of nodes
                    node_attention_mask = invert_mask(convert_length_to_mask(node_length, input_node_ids.max() + 1))
                    edge_attention_mask = invert_mask(convert_length_to_mask(edge_length, input_edge_ids.max() + 1))
                    word_attention_mask = invert_mask(convert_length_to_mask(word_length, decoder_whole_ids.max() + 1))
                    outputs_node = scatter_mean(hidden_states, input_node_ids, dim=1)  # batch * node_size (50) * embed_dim
                    assert outputs_node.size(1) == node_attention_mask.size(1)

                    # get each representation vector of edges
                    outputs_edge = scatter_mean(hidden_states, input_edge_ids, dim=1)  # batch * edge_size (60) * embed_dim
                    assert outputs_edge.size(1) == edge_attention_mask.size(1)
                    graph_attention_mask = torch.cat((node_attention_mask, edge_attention_mask), 1)  # batch * (node_length+edge_length)
                    outputs_graph = torch.cat((outputs_node, outputs_edge), 1)  # batch * (node_length+edge_length) * embed_dim

                    # get each representation vector of words
                    outputs_word = scatter_mean(sequence_output, decoder_whole_ids, dim=1)  # batch * output_length (128) * embed_dim
                    assert outputs_word.size(1) == word_attention_mask.size(1)
                    ot_dist = optimal_transport_dist(outputs_graph, outputs_word, graph_attention_mask,
                                                     word_attention_mask).to(outputs_graph)
                    loss = torch.mean(ot_dist)
                    return loss
        else:
            # Task 2: corrupted graph + complete text --> complete graph
            if is_training:
                loss_enc = nn.CrossEntropyLoss(ignore_index=-1)
                loss = loss_enc(lm_logits_encoder.view(-1, self.config.vocab_size), encoder_label.view(-1))
                return loss
        return decoder_outputs + encoder_outputs


class MyT5ForConditionalGeneration(T5ForConditionalGeneration):
    def __init__(self, config):
        super().__init__(config)
        self.model_dim = config.d_model

        self.shared = nn.Embedding(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        encoder_config.use_cache = False
        self.encoder = T5StructStack(encoder_config, self.shared)

        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        self.decoder = T5Stack(decoder_config, self.shared)

        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        self.init_weights()

    def forward(self, input_ids=None, attention_mask=None, encoder_outputs=None,
        decoder_input_ids=None, decoder_attention_mask=None,input_node_ids = None, input_edge_ids = None,
        node_length=None, edge_length=None, adj_matrix=None,
        decoder_past_key_value_states=None, use_cache=False, inputs_embeds=None, decoder_inputs_embeds=None, head_mask=None,
        output_attentions=None, output_hidden_states=None, is_training=False):

        if is_training:
            _decoder_input_ids = self._shift_right(decoder_input_ids)
        else:
            _decoder_input_ids = decoder_input_ids

        if encoder_outputs is None:
            # Convert encoder inputs in embeddings if needed
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                input_node_ids=input_node_ids,
                input_edge_ids=input_edge_ids,
                node_length=node_length,
                edge_length=edge_length,
                adj_matrix=adj_matrix,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )

        hidden_states = encoder_outputs[0]

        # If decoding with past key value states, only the last tokens
        # should be given as an input
        if decoder_past_key_value_states is not None:
            assert is_training is False, "Decoder should not use cached key value states when training."
            if _decoder_input_ids is not None:
                _decoder_input_ids = _decoder_input_ids[:, -1:]
            if decoder_inputs_embeds is not None:
                decoder_inputs_embeds = decoder_inputs_embeds[:, -1:]

        # Decode
        decoder_outputs = self.decoder(
            input_ids=_decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_value_states=decoder_past_key_value_states,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )

        # insert decoder past at right place
        # to speed up decoding
        if use_cache is True:
            past = ((encoder_outputs, decoder_outputs[1]),)
            decoder_outputs = decoder_outputs[:1] + past + decoder_outputs[2:]

        sequence_output = decoder_outputs[0]
        # Rescale output before projecting on vocab
        # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
        sequence_output_rescale = sequence_output * (self.model_dim ** -0.5)
        lm_logits_decoder = self.lm_head(sequence_output_rescale)

        decoder_outputs = (lm_logits_decoder,) + decoder_outputs[1:]  # Add hidden states and attention if they are here

        if is_training:
            loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
            loss = loss_fct(lm_logits_decoder.view(-1, self.config.vocab_size), decoder_input_ids.view(-1))
            decoder_outputs = (loss,) + decoder_outputs
            return loss
        return decoder_outputs + encoder_outputs

    @torch.no_grad()
    def generate(
            self,
            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,
            input_node_ids=None,
            input_edge_ids=None,
            node_length=None,
            edge_length=None,
            adj_matrix=None,
            decoder_start_token_id: Optional[int] = None,
            use_cache: Optional[bool] = None,
            **model_specific_kwargs
    ) -> torch.LongTensor:
        r""" Generates sequences for models with a LM 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`_.

        .. _`Facebook's XLM beam search code`:
           https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529


        Parameters:

            input_ids: (`optional`) `torch.LongTensor` of shape `(batch_size, sequence_length)`
                The sequence used as a prompt for the generation. If `None` the method initializes
                it as an empty `torch.LongTensor` of shape `(1,)`.

            max_length: (`optional`) int
                The max length of the sequence to be generated.  Between `min_length` and infinity. Default to 20.

            min_length: (`optional`) int
                The min length of the sequence to be generated.  Between 0 and infinity. Default to 0.

            do_sample: (`optional`) bool
                If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.

            early_stopping: (`optional`) bool
                if set to `True` beam search is stopped when at least `num_beams` sentences finished per batch. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.

            num_beams: (`optional`) int
                Number of beams for beam search. Must be between 1 and infinity. 1 means no beam search. Default to 1.

            temperature: (`optional`) float
                The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.

            top_k: (`optional`) int
                The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.

            top_p: (`optional`) float
                The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.

            repetition_penalty: (`optional`) float
                The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0.

            pad_token_id: (`optional`) int
                Padding token. Default to specicic model pad_token_id or None if it does not exist.

            bos_token_id: (`optional`) int
                BOS token. Defaults to `bos_token_id` as defined in the models config.

            eos_token_id: (`optional`) int
                EOS token. Defaults to `eos_token_id` as defined in the models config.

            length_penalty: (`optional`) float
                Exponential penalty to the length. Default to 1.

            no_repeat_ngram_size: (`optional`) int
                If set to int > 0, all ngrams of size `no_repeat_ngram_size` can only occur once.
            bad_words_ids: (`optional`) list of lists of int
                `bad_words_ids` contains tokens 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 `tokenizer.encode(bad_word, add_prefix_space=True)`.

            num_return_sequences: (`optional`) int
                The number of independently computed returned sequences for each element in the batch. Default to 1.

            attention_mask (`optional`) obj: `torch.LongTensor` of same shape as `input_ids`
                Mask to avoid performing attention on padding token indices.
                Mask values selected in ``[0, 1]``:
                ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
                Defaults to `None`.

                `What are attention masks? <../glossary.html#attention-mask>`__

            decoder_start_token_id=None: (`optional`) int
                Start token id for the decoder. Defaults to ``decoder_start_token_id`` as defined the model's config or to the ``bos_token_id``
                if no ``decoder_start_token_id`` is found in the config.
                This is only relevant for encoder-decoder models.

            use_cache: (`optional`) bool
                If `use_cache` is True, past key values are used to speed up decoding if applicable to model. Defaults to `True`.

            model_specific_kwargs: (`optional`) dict
                Additional model specific kwargs will be forwarded to the `forward` function of the model.

        Return:

            output: `torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`
                sequence_length is either equal to max_length or shorter if all batches finished early due to the `eos_token_id`

        Examples::

            from transformers import AutoTokenizer, AutoModelForCausalLM

            tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
            model = AutoModelForCausalLM.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 = AutoModelForCausalLM.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 = AutoModelForCausalLM.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)  # 3 generate sequences using by 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 = AutoModelForCausalLM.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 = AutoModelForCausalLM.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:
            logger.warning(
                "Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id)
            )
            pad_token_id = eos_token_id

        # current position and 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

        # 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:
                decoder_start_token_id = bos_token_id

            assert (
                    decoder_start_token_id is not None
            ), "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()

            # add structural information to encoder
            encoder_outputs: tuple = encoder(input_ids, attention_mask=attention_mask, input_node_ids=input_node_ids, \
                                             input_edge_ids=input_edge_ids, node_length=node_length,
                                             edge_length=edge_length, adj_matrix=adj_matrix)

        # Expand input ids if num_beams > 1 or num_return_sequences > 1
        if num_return_sequences > 1 or num_beams > 1:
            input_ids_len = input_ids.shape[-1]
            input_ids = input_ids.unsqueeze(1).expand(batch_size, effective_batch_mult * num_beams, input_ids_len)
            attention_mask = attention_mask.unsqueeze(1).expand(
                batch_size, effective_batch_mult * num_beams, input_ids_len
            )

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

        if self.config.is_encoder_decoder:
            # create empty decoder_input_ids
            input_ids = torch.full(
                (effective_batch_size * num_beams, 1),
                decoder_start_token_id,
                dtype=torch.long,
                device=next(self.parameters()).device,
            )
            cur_len = 1

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

        else:
            encoder_outputs = None
            cur_len = input_ids.shape[-1]

        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,
                encoder_outputs=encoder_outputs,
                attention_mask=attention_mask,
                use_cache=use_cache,
                model_specific_kwargs=model_specific_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,
                encoder_outputs=encoder_outputs,
                attention_mask=attention_mask,
                use_cache=use_cache,
                model_specific_kwargs=model_specific_kwargs,
            )

        return output