Upload model

config.json

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+{
+  "_name_or_path": "google/flan-t5-large",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_t5.T5Config",
+    "AutoModel": "modeling_t5.T5EncoderModel"
+  },
+  "d_ff": 2816,
+  "d_kv": 64,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "glm-t5",
+  "n_positions": 512,
+  "num_decoder_layers": 24,
+  "num_heads": 16,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_additional_buckets": 3,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.27.3",
+  "use_cache": true,
+  "vocab_size": 32128
+}

configuration_t5.py

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+# coding=utf-8
+# Copyright 2020, The T5 Authors and HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" T5 model configuration"""
+
+from typing import Mapping
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.onnx import OnnxSeq2SeqConfigWithPast
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+## FROM-T5
+# T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+#     "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
+#     "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
+#     "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
+#     "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
+#     "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
+# }
+
+
+class T5Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to
+    instantiate a T5 model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the T5
+    [t5-small](https://huggingface.co/t5-small) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Arguments:
+        vocab_size (`int`, *optional*, defaults to 32128):
+            Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`].
+        d_model (`int`, *optional*, defaults to 512):
+            Size of the encoder layers and the pooler layer.
+        d_kv (`int`, *optional*, defaults to 64):
+            Size of the key, query, value projections per attention head. The `inner_dim` of the projection layer will
+            be defined as `num_heads * d_kv`.
+        d_ff (`int`, *optional*, defaults to 2048):
+            Size of the intermediate feed forward layer in each `T5Block`.
+        num_layers (`int`, *optional*, defaults to 6):
+            Number of hidden layers in the Transformer encoder.
+        num_decoder_layers (`int`, *optional*):
+            Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
+        num_heads (`int`, *optional*, defaults to 8):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        relative_attention_num_buckets (`int`, *optional*, defaults to 32):
+            The number of buckets to use for each attention layer.
+        relative_attention_max_distance (`int`, *optional*, defaults to 128):
+            The maximum distance of the longer sequences for the bucket separation.
+        dropout_rate (`float`, *optional*, defaults to 0.1):
+            The ratio for all dropout layers.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        initializer_factor (`float`, *optional*, defaults to 1):
+            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+            testing).
+        feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
+            Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the
+            `"gated-gelu"` feed forward projection. Original T5 uses `"relu"`.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+    """
+    model_type = "glm-t5"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
+
+    def __init__(
+        self,
+        vocab_size=32128,
+        d_model=512,
+        d_kv=64,
+        d_ff=2048,
+        num_layers=6,
+        num_decoder_layers=None,
+        num_heads=8,
+        relative_attention_num_buckets=32,
+        relative_attention_max_distance=128,
+        dropout_rate=0.1,
+        layer_norm_epsilon=1e-6,
+        initializer_factor=1.0,
+        feed_forward_proj="relu",
+        is_encoder_decoder=True,
+        use_cache=True,
+        pad_token_id=0,
+        eos_token_id=1,
+        # GLM parameters
+        relative_attention_num_additional_buckets=0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.d_model = d_model
+        self.d_kv = d_kv
+        self.d_ff = d_ff
+        self.num_layers = num_layers
+        self.num_decoder_layers = (
+            num_decoder_layers if num_decoder_layers is not None else self.num_layers
+        )  # default = symmetry
+        self.num_heads = num_heads
+        self.relative_attention_num_buckets = relative_attention_num_buckets
+        self.relative_attention_max_distance = relative_attention_max_distance
+        self.dropout_rate = dropout_rate
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_factor = initializer_factor
+        self.feed_forward_proj = feed_forward_proj
+        self.use_cache = use_cache
+        self.relative_attention_num_additional_buckets = relative_attention_num_additional_buckets
+
+        act_info = self.feed_forward_proj.split("-")
+        self.dense_act_fn = act_info[-1]
+        self.is_gated_act = act_info[0] == "gated"
+
+        if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
+            raise ValueError(
+                f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
+                "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
+                "'gated-gelu' or 'relu'"
+            )
+
+        # for backwards compatibility
+        if feed_forward_proj == "gated-gelu":
+            self.dense_act_fn = "gelu_new"
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            is_encoder_decoder=is_encoder_decoder,
+            **kwargs,
+        )
+

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35adaf2acd3f4fa17fd690ea4c3941499905f91b90a8382165f374e35cb7775b
+size 1364997113

wrapper_functions.py

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+import torch
+import torch.nn as nn
+from functools import partial, cache
+from argparse import Namespace
+from typing import List, Tuple, Dict, Union, Optional
+from itertools import chain
+import random
+from typing import Literal
+
+from transformers import T5Tokenizer
+
+class Graph():
+    """
+    A graph class.
+    :param g: A list of tuples, where each tuple is a triple (head, r, tail).
+    """
+    def __init__(
+            self, 
+            g: List[Tuple[str,str,str]] = []
+        ):
+        self.g = g
+        self.concepts = self.get_concepts()  # list of all concepts in the graph
+        self.relations = self.get_relations()  # list of all relations in the graph
+        self.relations_multiple = self.get_relations_multiple()  # list of all relations in the graph, including duplicate relations
+
+    @property
+    def g(self) -> List[Tuple[str,str,str]]:
+        return self._g
+
+    @g.setter
+    def g(self, g: List[Tuple[str,str,str]]):
+        self._g = g
+
+    def num_triplets(self) -> int:
+        """
+        Get the number of triplets in the graph.
+        """
+        return len(self.g)
+
+    def get_concepts(self) -> List[str]:
+        """
+        Get the concepts in the graph.
+        """
+        concepts = list(set([triplet[i] for triplet in self.g for i in [0, 2]]))
+        concepts.sort()  # not necessary but makes debugging easier
+        return concepts    
+    
+    def get_relations(self) -> List[str]:
+        """
+        Get the relations in the graph.
+        """
+        relations = list(set(self.get_relations_multiple()))
+        relations.sort()  # not necessary but makes debugging easier
+        return relations
+    
+    def get_relations_multiple(self) -> List[str]:
+        """
+        Get the relations in the graph, including duplicate relations.
+        """
+        relations = [triplet[1] for triplet in self.g]
+        return relations
+
+    def __str__(self):
+        out_str = '\n'.join([str(triplet) for triplet in self.g])        
+        return out_str
+
+class Data(Namespace):
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.__dict__.update(kwargs)
+
+def get_dummy_graph(num_triplets:int=3) -> Graph:
+    g = [
+        ("dog", "IsA", "animal"),
+        ("cat", "IsA", "animal"),
+        ("black poodle", "IsA", "dog"),
+        ("black cat", "IsA", "cat"),
+    ]
+    assert num_triplets <=4, "num_triplets must be <= 4"
+    g = g[:num_triplets]
+    g = Graph(g)
+    return g
+
+def r2nl(r: str) -> str:
+    """
+    Convert a relation to a natural language string. Can be used to implement necessary changes in the data.
+    """
+    return r
+
+def _get_str2tok(g:Graph, tokenizer: T5Tokenizer) -> dict[str, list[int]]:
+    """
+    Get a dictionary that maps strings to tokens.
+    """
+    # tokenize concepts and relations
+    c_tok = tokenizer([r2nl(c) for c in g.concepts], padding=False)['input_ids']
+    r_tok = tokenizer([r2nl(r) for r in g.relations], padding=False)['input_ids']
+
+    tokens = c_tok + r_tok
+    node_names = g.concepts + g.relations  # these are not necessarily all nodes in the Levi Graph, as relations can occur more than once
+    assert len(tokens) == len(node_names), f"{len(tokens) = }, {len(node_names) = }"
+
+    # remove end-of-sequence token
+    tokens = [toks[:-1] if toks[-1] == tokenizer.eos_token_id else toks for toks in tokens]
+    
+    # create a dictionary mapping concepts and relations to their tokenized forms
+    str2tok = {node: tok for node, tok in zip(node_names, tokens)}
+    str2tok['</s>'] = [tokenizer.eos_token_id]
+    return str2tok
+
+def _get_graphT5_input_sequence(g:Graph, str2tok:dict, use_eos:bool) -> Tuple[list, dict]:
+    # get input sequence (i.e. sequence that will be fed into the model for this graph)
+    all_nodes = g.relations_multiple + g.concepts  # list of all concepts and relations that will be in the final sequence (i.e. all nodes of the Levi Graph)  # the order of nodes is first all relations (in the order that they appear in g.g), and then all concepts (in alphabetical order. though here the order is not important)
+    
+    if use_eos:
+        all_nodes.append('</s>')
+
+    all_tokens = [str2tok[node] for node in all_nodes]  # list of length #nodes, where each element is a list of token ids
+    indices = {node: [] for node in all_nodes}  # dictionary mapping each node to its start-index and end- in the sequence. Keys are nodes, values are lists of tuples (start_index, end_index). The lists have a length of 1 for concepts and are as long as the number of occurances of the relation in the graph for relations.  # WARNING: this assumes that concepts and realtions have different names. This not always the case for REBEL. For concept_indices this is fixed. 
+    num_relation_tokens = sum([len(token) for token in all_tokens[:len(g.relations_multiple)]])  # number of tokens that are relations
+    num_concept_tokens = sum([len(token) for token in all_tokens[len(g.relations_multiple):len(g.relations_multiple)+len(g.concepts)]])  # number of tokens that are concepts
+    num_eos_tokens = 1 if use_eos else 0
+
+    is_concept = torch.tensor([False] * num_relation_tokens + [True] * num_concept_tokens + [False] * num_eos_tokens, dtype=torch.bool)  # tensor of length #nodes, where each element is True if the node is a concept and False if it is a relation
+    index_counter = 0
+    assert len(all_nodes) == len(all_tokens), (all_nodes, all_tokens)
+
+    for node, token in zip(all_nodes, all_tokens):
+        indices[node].append((index_counter, index_counter + len(token)))
+        # assert is_concept[index_counter:index_counter+len(token)].all() == (node in g.concepts), f"{is_concept = }, {node = }, {g.concepts = }, {index_counter = }, {len(token) = }, {is_concept[index_counter:index_counter+len(token)] = }"
+        index_counter += len(token)
+
+    concept_indices = {node: [indices[node][-1]] for node in g.concepts}  # [-1] and reput in list in case relations have the same name as a concept (concepts are put in last). 
+    sequence = torch.tensor(list(chain.from_iterable(all_tokens)), dtype=torch.long)
+    sequence = sequence.unsqueeze(0)  # add batch dimension
+    is_concept = is_concept.unsqueeze(0)  # add batch dimension
+    return sequence, indices, is_concept, concept_indices
+
+def _get_graphT5_relativeposition_sparsitymask(g:Graph, indices:dict, sequence_length:int, use_eos:bool, eos:str) -> Tuple[torch.Tensor, torch.Tensor]:
+    ### get relative position of each node in the sequence, as well as the sparsity mask ###
+    # initialize relative position matrix)
+    relative_position = torch.zeros(size=(sequence_length, sequence_length), dtype=torch.long) 
+    # initialize sparsity mask
+    sparsity_mask = torch.zeros(size=(sequence_length, sequence_length), dtype=torch.bool)
+    # initialize use_additional_bucket
+    use_additional_bucket = torch.zeros(size=(sequence_length, sequence_length), dtype=torch.bool)
+    
+    # relative positions / sparsity within each node
+    for start, end in chain.from_iterable(indices.values()):
+        relative_position[start:end, start:end] = _get_relative_position(end-start)
+        sparsity_mask[start:end, start:end] = True
+
+    # relative position between nodes of the same triplet
+    relation_counter = {relation: 0 for relation in g.relations}  # dictionary mapping each relation to the number of times it has already appeared in the graph
+    for triplet in g.g:
+        pos_h = indices[triplet[0]][0]  # position of head; tuple (start_index, end_index)
+        pos_r = indices[triplet[1]][relation_counter[triplet[1]]]  # position of relation; tuple (start_index, end_index)
+        pos_t = indices[triplet[2]][0]  # position of tail; tuple (start_index, end_index)
+        
+        l_h, l_r = pos_h[1] - pos_h[0], pos_r[1] - pos_r[0]  # length (i.e. number of tokens) of head and relation
+
+        # iterate over all combinations of tokens in each triplet. This implementation is not very elegant, but it is sufficiently fast.
+        for ih, ph in enumerate(range(pos_h[0], pos_h[1])):  # iterate over all head tokens
+            for ir, pr in enumerate(range(pos_r[0], pos_r[1])):  # iterate over all relation tokens
+                relative_position[ph, pr] = l_h - ih + ir
+                relative_position[pr, ph] = - (l_h - ih + ir)
+                sparsity_mask[ph, pr] = True
+                sparsity_mask[pr, ph] = True
+            for it, pt in enumerate(range(pos_t[0], pos_t[1])):  # iterate over all tail tokens
+                relative_position[ph, pt] = l_h - ih + l_r + it
+                relative_position[pt, ph] = - (l_h - ih + l_r + it)
+                sparsity_mask[ph, pt] = True
+                sparsity_mask[pt, ph] = True
+        for ir, pr in enumerate(range(pos_r[0], pos_r[1])):  # iterate over all relation tokens
+            for it, pt in enumerate(range(pos_t[0], pos_t[1])):  # iterate over all tail tokens
+                relative_position[pr, pt] = l_r - ir + it
+                relative_position[pt, pr] = - (l_r - ir + it)
+                sparsity_mask[pr, pt] = True
+                sparsity_mask[pt, pr] = True
+
+        relation_counter[triplet[1]] += 1  # next time when that relation comes, then the next tokens will be used
+    
+    if use_eos:
+        assert len(indices['</s>']) == 1, f"{indices['</s>'] = } should have length 1"
+        pos_eos = indices['</s>'][0]  # position of head; tuple (start_index, end_index)
+        assert pos_eos[0] + 1 == pos_eos[1], pos_eos
+        pos_eos = pos_eos[0]  # position of eos token
+
+        if eos == 'bidirectional':
+            relative_position[:, pos_eos] = +1e6
+            relative_position[pos_eos, :] = -1e6
+            relative_position[pos_eos, pos_eos] = 0
+            sparsity_mask[:, pos_eos] = True
+            sparsity_mask[pos_eos, :] = True
+        elif eos == 'unidirectional':
+            relative_position[:, pos_eos] = 1e6
+            relative_position[pos_eos, pos_eos] = 0
+            sparsity_mask[pos_eos, :] = False  # no messages from eos to other tokens
+            sparsity_mask[:, pos_eos] = True
+        else:
+            raise ValueError(f'{eos = } is not a valid option.')
+    
+    relative_position = relative_position.unsqueeze(0)  # add batch dimension
+    sparsity_mask = sparsity_mask.unsqueeze(0)  # add batch dimension
+    use_additional_bucket = use_additional_bucket.unsqueeze(0)  # add batch dimension
+    return relative_position, sparsity_mask, use_additional_bucket
+
+def _get_global_graphT5_relativeposition_sparsitymask(g:Graph, indices:dict, sequence_length:int, use_eos:bool, eos:str) -> Tuple[torch.Tensor, torch.Tensor]:
+    ### get relative position of each node in the sequence, as well as the sparsity mask ###
+    # initialize relative position matrix)
+    # relative_position = torch.ones(size=(sequence_length, sequence_length), dtype=torch.long) * 1e6  # technically should be float('inf'), but it does not matter
+    relative_position = torch.zeros(size=(sequence_length, sequence_length), dtype=torch.long)
+    # initialize sparsity mask
+    sparsity_mask = torch.ones(size=(sequence_length, sequence_length), dtype=torch.bool)  # could switch to None, but then code has to be updated accordingly (in particular get_batch)
+    # initialize use_additional_bucket
+    use_additional_bucket = torch.ones(size=(sequence_length, sequence_length), dtype=torch.bool)
+
+    # relative positions / sparsity within each node
+    for start, end in chain.from_iterable(indices.values()):
+        relative_position[start:end, start:end] = _get_relative_position(end-start)
+        use_additional_bucket[start:end, start:end] = False
+
+    # relative position between nodes of the same triplet
+    relation_counter = {relation: 0 for relation in g.relations}  # dictionary mapping each relation to the number of times it has already appeared in the graph
+    for triplet in g.g:
+        pos_h = indices[triplet[0]][0]  # position of head; tuple (start_index, end_index)
+        pos_r = indices[triplet[1]][relation_counter[triplet[1]]]  # position of relation; tuple (start_index, end_index)
+        pos_t = indices[triplet[2]][0]  # position of tail; tuple (start_index, end_index)
+        
+        l_h, l_r = pos_h[1] - pos_h[0], pos_r[1] - pos_r[0]  # length (i.e. number of tokens) of head and relation
+
+        # iterate over all combinations of tokens in each triplet. This implementation is not very elegant, but it works.
+        for ih, ph in enumerate(range(pos_h[0], pos_h[1])):  # iterate over all head tokens
+            for ir, pr in enumerate(range(pos_r[0], pos_r[1])):  # iterate over all relation tokens
+                relative_position[ph, pr] = l_h - ih + ir
+                relative_position[pr, ph] = - (l_h - ih + ir)
+                use_additional_bucket[ph, pr] = False
+                use_additional_bucket[pr, ph] = False
+            for it, pt in enumerate(range(pos_t[0], pos_t[1])):  # iterate over all tail tokens
+                relative_position[ph, pt] = l_h - ih + l_r + it
+                relative_position[pt, ph] = - (l_h - ih + l_r + it)
+                use_additional_bucket[ph, pt] = False
+                use_additional_bucket[pt, ph] = False
+        for ir, pr in enumerate(range(pos_r[0], pos_r[1])):  # iterate over all relation tokens
+            for it, pt in enumerate(range(pos_t[0], pos_t[1])):  # iterate over all tail tokens
+                relative_position[pr, pt] = l_r - ir + it
+                relative_position[pt, pr] = - (l_r - ir + it)
+                use_additional_bucket[pr, pt] = False
+                use_additional_bucket[pt, pr] = False
+
+        relation_counter[triplet[1]] += 1  # next time when that relation comes, then the next tokens will be used
+        if use_eos:
+            assert len(indices['</s>']) == 1, f"{indices['</s>'] = } should have length 1"
+            pos_eos = indices['</s>'][0]  # position of head; tuple (start_index, end_index)
+            assert pos_eos[0] + 1 == pos_eos[1], pos_eos
+            pos_eos = pos_eos[0]  # position of eos token
+
+            if eos == 'bidirectional':
+                relative_position[:, pos_eos] = +1e6
+                relative_position[pos_eos, :] = -1e6
+                relative_position[pos_eos, pos_eos] = 0
+                sparsity_mask[:, pos_eos] = True
+                sparsity_mask[pos_eos, :] = True
+                use_additional_bucket[:, pos_eos] = False
+                use_additional_bucket[pos_eos, :] = False
+            elif eos == 'unidirectional':
+                relative_position[:, pos_eos] = 1e6
+                relative_position[pos_eos, pos_eos] = 0
+                sparsity_mask[pos_eos, :] = False  # no messages from eos to other tokens
+                sparsity_mask[:, pos_eos] = True
+                use_additional_bucket[:, pos_eos] = False
+                use_additional_bucket[pos_eos, :] = False
+            else:
+                raise ValueError(f'{eos = } is not a valid option.')
+    
+    relative_position = relative_position.unsqueeze(0)  # add batch dimension
+    sparsity_mask = sparsity_mask.unsqueeze(0)  # add batch dimension
+    use_additional_bucket = use_additional_bucket.unsqueeze(0)  # add batch dimension
+    return relative_position, sparsity_mask, use_additional_bucket
+
+def graph_to_graphT5(g:Graph, tokenizer:T5Tokenizer, how:str, eos:str)->Data:
+    """
+    Convert a graph to a graphT5 input.
+    :param g: graph
+    :param tokenizer: tokenizer
+    :param how: how to represent the graph. Can be 'local' or 'global' for lGLM and gGLM respectively.
+    :param eos: end-of-sequence token. Can be `False` for not using an eos token. When using an eos token, there are two ways to use it: `bidirectional` means that the eos token is connected to every other node in the graph, with a relative position of positive infinity (from node to eos) or negative infinity (from eos to node). `unidirectional` means that the eos token is connected to every node in the graph with a relative position of positive infinity (from node to eos), but not the other way around (i.e. no connection from eos to other node). This means, that nodes do not get messages from the eos token, which perceives locality when using the local GLM
+    """
+    if not isinstance(g, Graph):
+        g = Graph(g)
+    eos = str(eos)
+    assert eos in ['False', 'bidirectional', 'unidirectional'], f"{eos = } must be either 'False', 'bidirectional', or 'unidirectional'"
+    use_eos:bool = eos != 'False'
+
+    str2tok = _get_str2tok(g, tokenizer)  # get a dictionary mapping concepts and relations to their tokenized forms
+
+    sequence, indices, is_concept, concept_indices = _get_graphT5_input_sequence(g, str2tok, use_eos)  # get input sequence (i.e. sequence that will be fed into the model for this graph
+    sequence_length = sequence.shape[1]
+
+    if how == 'local':
+        relative_position, sparsity_mask, use_additional_bucket = _get_graphT5_relativeposition_sparsitymask(g, indices, sequence_length, use_eos, eos)
+        num_additional_buckets = 0  # lGLM does not use additional buckets
+    elif how == 'global':
+        relative_position, sparsity_mask, use_additional_bucket = _get_global_graphT5_relativeposition_sparsitymask(g, indices, sequence_length, use_eos, eos)
+        num_additional_buckets = 1  # gGLM uses 1 additional bucket for long-ranged G2G connections
+    else:
+        raise ValueError(f"how must be either 'local' or 'global', but is {how}")
+
+    input_ids = sequence
+
+    data = Data(input_ids=input_ids, relative_position=relative_position, sparsity_mask=sparsity_mask, use_additional_bucket=use_additional_bucket, indices=indices, is_concept=is_concept, concept_indices=concept_indices, num_additional_buckets=num_additional_buckets)
+
+    return data
+
+@cache
+def _get_relative_position(size):
+    return torch.tensor([[i - j for i in range(size)] for j in range(size)], dtype=torch.long)
+
+def get_embedding(
+        sequence_embedding: torch.Tensor,
+        indices: Dict[str, List[Tuple[int, int]]],
+        concept: str,
+        embedding_aggregation: str = "mean",
+    ):
+    """
+    Returns the embedding of a concept.
+    :param sequence_embedding: the embedding of the whole sequence. shape: (sequence_length, embedding_size)
+    :param indices: dictionary mapping each node to its start-index and end- in the sequence. Keys are nodes, values are lists of tuples (start_index, end_index). The lists have a length of 1 for concepts.
+    :param concept: the concept for which the embedding should be returned
+    :param embedding_aggregation: how the embedding of a concept should be aggregated. Either "mean" or "seq". "mean" returns the mean of all tokens of the concept. "seq" returns the embeddings of the all token of the concept.
+    :return: the aggregated embedding of the concept. shape (1, embedding_size) or (number_of_tokens, embedding_size). 
+    """
+    assert concept in indices.keys(), f"{concept = } is not a node in the graph. {indices = }"
+    assert len(indices[concept]) == 1, f"{concept = } is not a concept, as concepts occur only once in the graph. {indices = }"
+
+    start, end = indices[concept][0]
+    sequence_embedding = sequence_embedding[start:end, :]
+    if embedding_aggregation == "mean":
+        return torch.mean(sequence_embedding, dim=0, keepdim=True)
+    elif embedding_aggregation == "seq":
+        return sequence_embedding
+    else:
+        raise NotImplementedError(f"{embedding_aggregation = } is not supported. Use either 'mean' or 'seq'.")
+
+def add_text_to_graph_data(data, text, tokenizer, use_text):
+    if use_text in {'False', '', False, None}:
+        return None
+
+    text_seq = torch.tensor(tokenizer(text, padding=False)['input_ids']).unsqueeze(0)
+    new_input_ids = torch.cat([data.input_ids, text_seq], dim=1)
+
+    old_seq_len = data.input_ids.shape[1]
+    text_seq_len = text_seq.shape[1]
+    new_seq_len = new_input_ids.shape[1]
+
+    new_is_graph = torch.zeros(size=(1, new_seq_len), dtype=torch.bool)
+    new_is_graph[:, :old_seq_len] = True
+
+    if data.relative_position is None:  # sequence transformer
+        assert data.sparsity_mask is None
+        assert data.use_additional_bucket is None
+        data.input_ids = new_input_ids
+        data.is_graph = new_is_graph
+        return None
+
+    new_relative_position = torch.zeros(size=(1, new_seq_len, new_seq_len), dtype=data.relative_position.dtype)
+    new_relative_position[:, :old_seq_len, :old_seq_len] = data.relative_position
+    new_relative_position[:, old_seq_len:, old_seq_len:] = _get_relative_position(text_seq_len)
+
+    new_sparsity_mask = torch.zeros(size=(1, new_seq_len, new_seq_len), dtype=data.sparsity_mask.dtype)
+    new_sparsity_mask[:, :old_seq_len, :old_seq_len] = data.sparsity_mask
+    new_sparsity_mask[:, old_seq_len:, old_seq_len:] = True
+    
+    new_use_additional_bucket = torch.zeros(size=(1, new_seq_len, new_seq_len), dtype=data.use_additional_bucket.dtype)
+    new_use_additional_bucket[:, :old_seq_len, :old_seq_len] = data.use_additional_bucket
+    new_use_additional_bucket[:, old_seq_len:, old_seq_len:] = False  # could change that if we want T2T and local G2G relations to be learned separately
+
+    if use_text in {'FullyConnected', True}:
+        new_sparsity_mask[:, old_seq_len:, :old_seq_len] = True
+        new_sparsity_mask[:, :old_seq_len, old_seq_len:] = True
+
+        new_use_additional_bucket[:, old_seq_len:, :old_seq_len] = True
+        new_use_additional_bucket[:, :old_seq_len, old_seq_len:] = True
+
+        new_relative_position[:, old_seq_len:, :old_seq_len] = data.num_additional_buckets
+        new_relative_position[:, :old_seq_len, old_seq_len:] = data.num_additional_buckets + 1
+        
+        new_num_additional_buckets = data.num_additional_buckets + 2
+    else:
+        raise ValueError(f"unknown use_text {use_text} (type {type(use_text)})")
+
+    data.input_ids = new_input_ids
+    data.relative_position = new_relative_position
+    data.sparsity_mask = new_sparsity_mask
+    data.use_additional_bucket = new_use_additional_bucket
+    data.num_additional_buckets = new_num_additional_buckets
+    data.is_graph = new_is_graph
+    return None
+
+class DataProcessor():
+    @staticmethod
+    def encode_graph(tokenizer, g:Union[Graph,list[tuple[str,str,str]]], text:Optional[str]=None, how:Literal['global', 'local']='global', eos:str="False")->Data:
+        """
+        convert graph to suitable input for the model. 
+        :param tokenizer: tokenizer
+        :param g: graph
+        :param text: text to add to the graph. Can be None if no text should be added. 
+        :param how: how to represent the graph. Can be 'local' or 'global' for lGLM and gGLM respectively.
+        :param eos: end-of-sequence token. Can be `False` for not using an eos token. This is the method used in the paper. When using an eos token, there are two ways to use it: `bidirectional` means that the eos token is connected to every other node in the graph. `unidirectional` means that the eos token is connected to every node in the graph (from node to eos), but not the other way around (i.e. no connection from eos to other node). This means, that nodes do not get messages from the eos token, which perceives locality when using the local GLM
+        :return: Data object
+        """
+        if not isinstance(g, Graph):
+            g = Graph(g)
+        data = graph_to_graphT5(g, tokenizer, how, eos)
+        if text is not None:
+            add_text_to_graph_data(data, text, tokenizer, use_text=True)
+        return data
+    
+    @staticmethod
+    def to_batch(data_instances:list[Data], tokenizer, max_seq_len:Optional[int]=None, device:str='cpu', **kwargs)->dict: 
+        """
+        converts list of data instances to batched inputs for GLM forward call. 
+        :param datas: list of Data instances
+        :param max_seq_len: maximum sequence length
+        :param tokenizer: tokenizer
+        :param device: device
+        :return: dictionary with keys 'input_ids', 'relative_position', 'sparsity_mask', and 'use_additional_bucket'
+        """
+        current_max_seq_len = max([data.input_ids.shape[1] for data in data_instances])
+        if max_seq_len is None:
+            max_seq_len = current_max_seq_len
+        else:
+            max_seq_len = min(max_seq_len, current_max_seq_len)
+
+        if data_instances[0].relative_position is None:
+            assert data_instances[0].sparsity_mask is None
+            assert data_instances[0].use_additional_bucket is None
+            is_sequence_transformer = True
+        else:
+            assert data_instances[0].sparsity_mask is not None
+            assert data_instances[0].use_additional_bucket is not None
+            is_sequence_transformer = False
+
+        # intialize tensors
+        input_ids = torch.ones((len(data_instances), max_seq_len), dtype=torch.long, device=device) * tokenizer.pad_token_id
+        if is_sequence_transformer:
+            relative_position = None 
+            sparsity_mask = None 
+            use_additional_bucket = None 
+        else:
+            relative_position = torch.zeros((len(data_instances), max_seq_len, max_seq_len), dtype=torch.long, device=device)
+            sparsity_mask = torch.zeros((len(data_instances), max_seq_len, max_seq_len), dtype=torch.bool, device=device)
+            use_additional_bucket = torch.zeros((len(data_instances), max_seq_len, max_seq_len), dtype=torch.bool, device=device)
+
+        # fill tensors
+        for i, data in enumerate(data_instances):
+            instance_len = min(data.input_ids.shape[1], max_seq_len)
+            input_ids[i, :instance_len] = data.input_ids[:, :instance_len]
+            if not is_sequence_transformer:
+                relative_position[i, :instance_len, :instance_len] = data.relative_position[:, :instance_len, :instance_len]
+                sparsity_mask[i, :instance_len, :instance_len] = data.sparsity_mask[:, :instance_len, :instance_len]
+                use_additional_bucket[i, :instance_len, :instance_len] = data.use_additional_bucket[:, :instance_len, :instance_len]
+
+        model_input = {
+            'input_ids': input_ids,
+            'relative_position': relative_position,
+            'sparsity_mask': sparsity_mask,
+            'use_additional_bucket': use_additional_bucket,
+            **kwargs
+        }
+        return model_input
+
+    @staticmethod
+    def get_embedding(sequence_embedding:torch.Tensor, indices:Dict[str,List[Tuple[int, int]]], concept:str, embedding_aggregation:str="mean"):
+        """
+        Returns embedding of a concept.
+        :param sequence_embedding: the embedding of the whole sequence. shape: (sequence_length, embedding_size)
+        :param indices: dictionary mapping each node to its start- and end-index in the sequence. Keys are nodes, values are lists of tuples (start_index, end_index). The lists have a length of 1 for concepts. indices is part of the Data object. 
+        :param concept: the concept for which the embedding should be returned. 
+        :param embedding_aggregation: how the embedding of a concept should be aggregated. Either "mean" or "seq". "mean" returns the mean of all tokens of the concept. "seq" returns the embeddings of the all token of the concept.
+        :return: the aggregated embedding of the concept. shape (1, embedding_size) or (number_of_tokens, embedding_size).
+        """
+        return get_embedding(sequence_embedding, indices, concept, embedding_aggregation)
+