add model

config.json

@@ -0,0 +1,39 @@
+{
+  "architectures": [
+    "StructRoberta"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoConfig": "modeling_structroberta.StructRobertaConfig",
+    "AutoModelForMaskedLM": "modeling_structroberta.StructRoberta"
+  },
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "conv_size": 9,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "n_cntxt_layers": 4,
+  "n_cntxt_layers_2": 0,
+  "n_parser_layers": 6,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 8,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "relations": [
+    "head",
+    "child"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32000,
+  "weight_act": "softmax"
+}

modeling_structroberta.py

@@ -0,0 +1,2146 @@
+import copy
+import math
+from typing import List, Optional, Tuple, Union
+import torch
+import torch.utils.checkpoint
+from packaging import version
+from torch import nn
+import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
+from transformers.activations import ACT2FN, gelu
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+    SequenceClassifierOutput
+)
+from transformers.modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from transformers.utils import logging
+from transformers import RobertaConfig
+
+
+logger = logging.get_logger(__name__)
+ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "roberta-base",
+    "roberta-large",
+    "roberta-large-mnli",
+    "distilroberta-base",
+    "roberta-base-openai-detector",
+    "roberta-large-openai-detector",
+    # See all RoBERTa models at https://huggingface.co/models?filter=roberta
+]
+
+
+class StructRobertaConfig(RobertaConfig):
+    model_type = "roberta"
+
+    def __init__(
+        self,
+        n_parser_layers=4,
+        conv_size=9,
+        relations=("head", "child"),
+        weight_act="softmax",
+        n_cntxt_layers=3,
+        n_cntxt_layers_2=0,
+        **kwargs,):
+        
+        super().__init__(**kwargs)
+        self.n_cntxt_layers = n_cntxt_layers
+        self.n_parser_layers = n_parser_layers
+        self.n_cntxt_layers_2 = n_cntxt_layers_2
+        self.conv_size = conv_size
+        self.relations = relations
+        self.weight_act = weight_act
+
+class Conv1d(nn.Module):
+    """1D convolution layer."""
+
+    def __init__(self, hidden_size, kernel_size, dilation=1):
+        """Initialization.
+
+        Args:
+        hidden_size: dimension of input embeddings
+        kernel_size: convolution kernel size
+        dilation: the spacing between the kernel points
+        """
+        super(Conv1d, self).__init__()
+
+        if kernel_size % 2 == 0:
+            padding = (kernel_size // 2) * dilation
+            self.shift = True
+        else:
+            padding = ((kernel_size - 1) // 2) * dilation
+            self.shift = False
+        self.conv = nn.Conv1d(
+            hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation
+        )
+
+    def forward(self, x):
+        """Compute convolution.
+
+        Args:
+          x: input embeddings
+        Returns:
+          conv_output: convolution results
+        """
+
+        if self.shift:
+            return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:]
+        else:
+            return self.conv(x.transpose(1, 2)).transpose(1, 2)
+
+
+class RobertaEmbeddings(nn.Module):
+    """
+    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+    """
+
+    # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
+        self.position_embeddings = nn.Embedding(
+            config.max_position_embeddings, config.hidden_size
+        )
+        self.token_type_embeddings = nn.Embedding(
+            config.type_vocab_size, config.hidden_size
+        )
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.position_embedding_type = getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        self.register_buffer(
+            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))
+        )
+        if version.parse(torch.__version__) > version.parse("1.6.0"):
+            self.register_buffer(
+                "token_type_ids",
+                torch.zeros(self.position_ids.size(), dtype=torch.long),
+                persistent=False,
+            )
+
+        # End copy
+        self.padding_idx = config.pad_token_id
+        self.position_embeddings = nn.Embedding(
+            config.max_position_embeddings,
+            config.hidden_size,
+            padding_idx=self.padding_idx,
+        )
+
+    def forward(
+        self,
+        input_ids=None,
+        token_type_ids=None,
+        position_ids=None,
+        inputs_embeds=None,
+        past_key_values_length=0,
+    ):
+        if position_ids is None:
+            if input_ids is not None:
+                # Create the position ids from the input token ids. Any padded tokens remain padded.
+                position_ids = create_position_ids_from_input_ids(
+                    input_ids, self.padding_idx, past_key_values_length
+                )
+            else:
+                position_ids = self.create_position_ids_from_inputs_embeds(
+                    inputs_embeds
+                )
+
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
+        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
+        # issue #5664
+        if token_type_ids is None:
+            if hasattr(self, "token_type_ids"):
+                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
+                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
+                    input_shape[0], seq_length
+                )
+                token_type_ids = buffered_token_type_ids_expanded
+            else:
+                token_type_ids = torch.zeros(
+                    input_shape, dtype=torch.long, device=self.position_ids.device
+                )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + token_type_embeddings
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings += position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+        """
+        We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+        Args:
+            inputs_embeds: torch.Tensor
+
+        Returns: torch.Tensor
+        """
+        input_shape = inputs_embeds.size()[:-1]
+        sequence_length = input_shape[1]
+
+        position_ids = torch.arange(
+            self.padding_idx + 1,
+            sequence_length + self.padding_idx + 1,
+            dtype=torch.long,
+            device=inputs_embeds.device,
+        )
+        return position_ids.unsqueeze(0).expand(input_shape)
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta
+class RobertaSelfAttention(nn.Module):
+    def __init__(self, config, position_embedding_type=None):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
+            config, "embedding_size"
+        ):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = position_embedding_type or getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        if (
+            self.position_embedding_type == "relative_key"
+            or self.position_embedding_type == "relative_key_query"
+        ):
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(
+                2 * config.max_position_embeddings - 1, self.attention_head_size
+            )
+
+        self.is_decoder = config.is_decoder
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (
+            self.num_attention_heads,
+            self.attention_head_size,
+        )
+        x = x.view(new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
+        parser_att_mask=None,
+    ) -> Tuple[torch.Tensor]:
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention and past_key_value is not None:
+            # reuse k,v, cross_attentions
+            key_layer = past_key_value[0]
+            value_layer = past_key_value[1]
+            attention_mask = encoder_attention_mask
+        elif is_cross_attention:
+            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        if self.is_decoder:
+            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_layer, value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if (
+            self.position_embedding_type == "relative_key"
+            or self.position_embedding_type == "relative_key_query"
+        ):
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(
+                seq_length, dtype=torch.long, device=hidden_states.device
+            ).view(-1, 1)
+            position_ids_r = torch.arange(
+                seq_length, dtype=torch.long, device=hidden_states.device
+            ).view(1, -1)
+            distance = position_ids_l - position_ids_r
+            positional_embedding = self.distance_embedding(
+                distance + self.max_position_embeddings - 1
+            )
+            positional_embedding = positional_embedding.to(
+                dtype=query_layer.dtype
+            )  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum(
+                    "bhld,lrd->bhlr", query_layer, positional_embedding
+                )
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum(
+                    "bhld,lrd->bhlr", query_layer, positional_embedding
+                )
+                relative_position_scores_key = torch.einsum(
+                    "bhrd,lrd->bhlr", key_layer, positional_embedding
+                )
+                attention_scores = (
+                    attention_scores
+                    + relative_position_scores_query
+                    + relative_position_scores_key
+                )
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        if parser_att_mask is None:
+            # Normalize the attention scores to probabilities.
+            attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+        else:
+            attention_probs = torch.sigmoid(attention_scores) * parser_att_mask
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        outputs = (
+            (context_layer, attention_probs) if output_attentions else (context_layer,)
+        )
+
+        if self.is_decoder:
+            outputs = outputs + (past_key_value,)
+        return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
+class RobertaSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(
+        self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta
+class RobertaAttention(nn.Module):
+    def __init__(self, config, position_embedding_type=None):
+        super().__init__()
+        self.self = RobertaSelfAttention(
+            config, position_embedding_type=position_embedding_type
+        )
+        self.output = RobertaSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads,
+            self.self.num_attention_heads,
+            self.self.attention_head_size,
+            self.pruned_heads,
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = (
+            self.self.attention_head_size * self.self.num_attention_heads
+        )
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
+        parser_att_mask=None,
+    ) -> Tuple[torch.Tensor]:
+        self_outputs = self.self(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+            parser_att_mask=parser_att_mask,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[
+            1:
+        ]  # add attentions if we output them
+        return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
+class RobertaIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput
+class RobertaOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(
+        self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta
+class RobertaLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = RobertaAttention(config)
+        self.is_decoder = config.is_decoder
+        self.add_cross_attention = config.add_cross_attention
+        if self.add_cross_attention:
+            if not self.is_decoder:
+                raise ValueError(
+                    f"{self} should be used as a decoder model if cross attention is added"
+                )
+            self.crossattention = RobertaAttention(
+                config, position_embedding_type="absolute"
+            )
+        self.intermediate = RobertaIntermediate(config)
+        self.output = RobertaOutput(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
+        parser_att_mask=None,
+    ) -> Tuple[torch.Tensor]:
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        self_attn_past_key_value = (
+            past_key_value[:2] if past_key_value is not None else None
+        )
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            past_key_value=self_attn_past_key_value,
+            parser_att_mask=parser_att_mask,
+        )
+        attention_output = self_attention_outputs[0]
+
+        # if decoder, the last output is tuple of self-attn cache
+        if self.is_decoder:
+            outputs = self_attention_outputs[1:-1]
+            present_key_value = self_attention_outputs[-1]
+        else:
+            outputs = self_attention_outputs[
+                1:
+            ]  # add self attentions if we output attention weights
+
+        cross_attn_present_key_value = None
+        if self.is_decoder and encoder_hidden_states is not None:
+            if not hasattr(self, "crossattention"):
+                raise ValueError(
+                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
+                )
+
+            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
+            cross_attn_past_key_value = (
+                past_key_value[-2:] if past_key_value is not None else None
+            )
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                cross_attn_past_key_value,
+                output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = (
+                outputs + cross_attention_outputs[1:-1]
+            )  # add cross attentions if we output attention weights
+
+            # add cross-attn cache to positions 3,4 of present_key_value tuple
+            cross_attn_present_key_value = cross_attention_outputs[-1]
+            present_key_value = present_key_value + cross_attn_present_key_value
+
+        layer_output = apply_chunking_to_forward(
+            self.feed_forward_chunk,
+            self.chunk_size_feed_forward,
+            self.seq_len_dim,
+            attention_output,
+        )
+        outputs = (layer_output,) + outputs
+
+        # if decoder, return the attn key/values as the last output
+        if self.is_decoder:
+            outputs = outputs + (present_key_value,)
+
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta
+class RobertaEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList(
+            [RobertaLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+        parser_att_mask=None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = (
+            () if output_attentions and self.config.add_cross_attention else None
+        )
+
+        next_decoder_cache = () if use_cache else None
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                if use_cache:
+                    logger.warning(
+                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                if parser_att_mask is not None:
+                    layer_outputs = layer_module(
+                        hidden_states,
+                        attention_mask,
+                        layer_head_mask,
+                        encoder_hidden_states,
+                        encoder_attention_mask,
+                        past_key_value,
+                        output_attentions,
+                        parser_att_mask=parser_att_mask[i])
+                else:
+                    layer_outputs = layer_module(
+                        hidden_states,
+                        attention_mask,
+                        layer_head_mask,
+                        encoder_hidden_states,
+                        encoder_attention_mask,
+                        past_key_value,
+                        output_attentions,
+                        parser_att_mask=None)
+                    
+
+            hidden_states = layer_outputs[0]
+            if use_cache:
+                next_decoder_cache += (layer_outputs[-1],)
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+                if self.config.add_cross_attention:
+                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class RobertaPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class RobertaPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = RobertaConfig
+    base_model_prefix = "roberta"
+    supports_gradient_checkpointing = True
+
+    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            if module.bias is not None:
+                module.bias.data.zero_()
+                module.weight.data.fill_(1.0)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, RobertaEncoder):
+            module.gradient_checkpointing = value
+
+    def update_keys_to_ignore(self, config, del_keys_to_ignore):
+        """Remove some keys from ignore list"""
+        if not config.tie_word_embeddings:
+            # must make a new list, or the class variable gets modified!
+            self._keys_to_ignore_on_save = [
+                k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore
+            ]
+            self._keys_to_ignore_on_load_missing = [
+                k
+                for k in self._keys_to_ignore_on_load_missing
+                if k not in del_keys_to_ignore
+            ]
+
+
+ROBERTA_START_DOCSTRING = r"""
+
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`RobertaConfig`]): Model configuration class with all the parameters of the
+            model. Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+ROBERTA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+            1]`:
+
+            - 0 corresponds to a *sentence A* token,
+            - 1 corresponds to a *sentence B* token.
+
+            [What are token type IDs?](../glossary#token-type-ids)
+        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+class RobertaModel(RobertaPreTrainedModel):
+    """
+
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in *Attention is
+    all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz
+    Kaiser and Illia Polosukhin.
+
+    To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
+    to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
+    `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
+
+    .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762
+
+    """
+
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = RobertaEmbeddings(config)
+        self.encoder = RobertaEncoder(config)
+
+        self.pooler = RobertaPooler(config) if add_pooling_layer else None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    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)
+
+    # Copied from transformers.models.bert.modeling_bert.BertModel.forward
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        parser_att_mask=None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        r"""
+        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        """
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if self.config.is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        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()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        # past_key_values_length
+        past_key_values_length = (
+            past_key_values[0][0].shape[2] if past_key_values is not None else 0
+        )
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                ((batch_size, seq_length + past_key_values_length)), device=device
+            )
+
+        if token_type_ids is None:
+            if hasattr(self.embeddings, "token_type_ids"):
+                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
+                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
+                    batch_size, seq_length
+                )
+                token_type_ids = buffered_token_type_ids_expanded
+            else:
+                token_type_ids = torch.zeros(
+                    input_shape, dtype=torch.long, device=device
+                )
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+            attention_mask, input_shape, device
+        )
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            (
+                encoder_batch_size,
+                encoder_sequence_length,
+                _,
+            ) = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(
+                encoder_attention_mask
+            )
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            parser_att_mask=parser_att_mask,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = (
+            self.pooler(sequence_output) if self.pooler is not None else None
+        )
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+class StructRoberta(RobertaPreTrainedModel):
+    _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"]
+    _keys_to_ignore_on_load_missing = [
+        r"position_ids",
+        r"lm_head.decoder.weight",
+        r"lm_head.decoder.bias",
+    ]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        
+        if config.n_cntxt_layers > 0:
+            config_cntxt = copy.deepcopy(config)
+            config_cntxt.num_hidden_layers = config.n_cntxt_layers
+            
+            self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False)
+        
+        if config.n_cntxt_layers_2 > 0:
+            self.parser_layers_1 = nn.ModuleList(
+                [
+                    nn.Sequential(
+                        Conv1d(config.hidden_size, config.conv_size),
+                        nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                        nn.Tanh(),
+                    )
+                    for i in range(int(config.n_parser_layers/2))
+                ]
+            )
+
+            self.distance_ff_1 = nn.Sequential(
+                Conv1d(config.hidden_size, 2),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            self.height_ff_1 = nn.Sequential(
+                nn.Linear(config.hidden_size, config.hidden_size),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            n_rel = len(config.relations)
+            self._rel_weight_1 = nn.Parameter(
+                torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel))
+            )
+            self._rel_weight_1.data.normal_(0, 0.1)
+
+            self._scaler_1 = nn.Parameter(torch.zeros(2))
+            
+            config_cntxt_2 = copy.deepcopy(config)
+            config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2
+            
+            self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False)
+            
+            
+            self.parser_layers_2 = nn.ModuleList(
+                [
+                    nn.Sequential(
+                        Conv1d(config.hidden_size, config.conv_size),
+                        nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                        nn.Tanh(),
+                    )
+                    for i in range(int(config.n_parser_layers/2))
+                ]
+            )
+
+            self.distance_ff_2 = nn.Sequential(
+                Conv1d(config.hidden_size, 2),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            self.height_ff_2 = nn.Sequential(
+                nn.Linear(config.hidden_size, config.hidden_size),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            n_rel = len(config.relations)
+            self._rel_weight_2 = nn.Parameter(
+                torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel))
+            )
+            self._rel_weight_2.data.normal_(0, 0.1)
+
+            self._scaler_2 = nn.Parameter(torch.zeros(2))
+            
+        else:
+            self.parser_layers = nn.ModuleList(
+                [
+                    nn.Sequential(
+                        Conv1d(config.hidden_size, config.conv_size),
+                        nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                        nn.Tanh(),
+                    )
+                    for i in range(config.n_parser_layers)
+                ]
+            )
+
+            self.distance_ff = nn.Sequential(
+                Conv1d(config.hidden_size, 2),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            self.height_ff = nn.Sequential(
+                nn.Linear(config.hidden_size, config.hidden_size),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            n_rel = len(config.relations)
+            self._rel_weight = nn.Parameter(
+                torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel))
+            )
+            self._rel_weight.data.normal_(0, 0.1)
+
+            self._scaler = nn.Parameter(torch.zeros(2))
+
+        self.roberta = RobertaModel(config, add_pooling_layer=False)
+        
+        if config.n_cntxt_layers > 0:
+            self.cntxt_layers.embeddings = self.roberta.embeddings
+        if config.n_cntxt_layers_2 > 0:
+            self.cntxt_layers_2.embeddings = self.roberta.embeddings
+        
+        self.lm_head = RobertaLMHead(config)
+
+        self.pad = config.pad_token_id
+
+        # The LM head weights require special treatment only when they are tied with the word embeddings
+        self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    @property
+    def scaler(self):
+        return self._scaler.exp()
+    
+    @property
+    def scaler_1(self):
+        return self._scaler_1.exp()
+    
+    @property
+    def scaler_2(self):
+        return self._scaler_2.exp()
+
+    @property
+    def rel_weight(self):
+        if self.config.weight_act == "sigmoid":
+            return torch.sigmoid(self._rel_weight)
+        elif self.config.weight_act == "softmax":
+            return torch.softmax(self._rel_weight, dim=-1)
+        
+    @property
+    def rel_weight_1(self):
+        if self.config.weight_act == "sigmoid":
+            return torch.sigmoid(self._rel_weight_1)
+        elif self.config.weight_act == "softmax":
+            return torch.softmax(self._rel_weight_1, dim=-1)
+
+    
+    @property
+    def rel_weight_2(self):
+        if self.config.weight_act == "sigmoid":
+            return torch.sigmoid(self._rel_weight_2)
+        elif self.config.weight_act == "softmax":
+            return torch.softmax(self._rel_weight_2, dim=-1)
+
+
+    def compute_block(self, distance, height, n_cntxt_layers=0):
+        """Compute constituents from distance and height."""
+
+        if n_cntxt_layers>0:
+            if n_cntxt_layers == 1:
+              beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0]
+            elif n_cntxt_layers == 2:
+              beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0]
+        else:
+            beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+        gamma = torch.sigmoid(-beta_logits)
+        ones = torch.ones_like(gamma)
+
+        block_mask_left = cummin(
+            gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1
+        )
+        block_mask_left = block_mask_left - F.pad(
+            block_mask_left[:, :, :-1], (1, 0), value=0
+        )
+        block_mask_left.tril_(0)
+
+        block_mask_right = cummin(
+            gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1
+        )
+        block_mask_right = block_mask_right - F.pad(
+            block_mask_right[:, :, 1:], (0, 1), value=0
+        )
+        block_mask_right.triu_(0)
+
+        block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+        block = cumsum(block_mask_left).tril(0) + cumsum(
+            block_mask_right, reverse=True
+        ).triu(1)
+
+        return block_p, block
+
+    def compute_head(self, height, n_cntxt_layers=0):
+        """Estimate head for each constituent."""
+
+        _, length = height.size()
+        if n_cntxt_layers>0:
+          if n_cntxt_layers == 1:
+              head_logits = height * self.scaler_1[1]
+          elif n_cntxt_layers == 2:
+              head_logits = height * self.scaler_2[1]  
+        else:
+            head_logits = height * self.scaler[1]
+        index = torch.arange(length, device=height.device)
+
+        mask = (index[:, None, None] <= index[None, None, :]) * (
+            index[None, None, :] <= index[None, :, None]
+        )
+        head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+        head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+        head_p = torch.softmax(head_logits, dim=-1)
+
+        return head_p
+
+    def parse(self, x, embs=None, n_cntxt_layers=0):
+        """Parse input sentence.
+
+        Args:
+        x: input tokens (required).
+        pos: position for each token (optional).
+        Returns:
+        distance: syntactic distance
+        height: syntactic height
+        """
+
+        mask = x != self.pad
+        mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+        if embs is None:
+            h = self.roberta.embeddings(x)
+        else:
+            h = embs
+        
+        if n_cntxt_layers > 0:
+            if n_cntxt_layers == 1:
+                parser_layers = self.parser_layers_1
+                height_ff = self.height_ff_1
+                distance_ff = self.distance_ff_1
+            elif n_cntxt_layers == 2:
+                parser_layers = self.parser_layers_2
+                height_ff = self.height_ff_2
+                distance_ff = self.distance_ff_2
+            for i in range(int(self.config.n_parser_layers/2)):
+                h = h.masked_fill(~mask[:, :, None], 0)
+                h = parser_layers[i](h)
+
+            height = height_ff(h).squeeze(-1)
+            height.masked_fill_(~mask, -1e9)
+
+            distance = distance_ff(h).squeeze(-1)
+            distance.masked_fill_(~mask_shifted, 1e9)
+
+            # Calbrating the distance and height to the same level
+            length = distance.size(1)
+            height_max = height[:, None, :].expand(-1, length, -1)
+            height_max = torch.cummax(
+                height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1
+            )[0].triu(0)
+
+            margin_left = torch.relu(
+                F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max
+            )
+            margin_right = torch.relu(distance[:, None, :] - height_max)
+            margin = torch.where(
+                margin_left > margin_right, margin_right, margin_left
+            ).triu(0)
+
+            margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+            margin.masked_fill_(~margin_mask, 0)
+            margin = margin.max()
+
+            distance = distance - margin
+        else:
+            for i in range(self.config.n_parser_layers):
+                h = h.masked_fill(~mask[:, :, None], 0)
+                h = self.parser_layers[i](h)
+
+            height = self.height_ff(h).squeeze(-1)
+            height.masked_fill_(~mask, -1e9)
+
+            distance = self.distance_ff(h).squeeze(-1)
+            distance.masked_fill_(~mask_shifted, 1e9)
+
+            # Calbrating the distance and height to the same level
+            length = distance.size(1)
+            height_max = height[:, None, :].expand(-1, length, -1)
+            height_max = torch.cummax(
+                height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1
+            )[0].triu(0)
+
+            margin_left = torch.relu(
+                F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max
+            )
+            margin_right = torch.relu(distance[:, None, :] - height_max)
+            margin = torch.where(
+                margin_left > margin_right, margin_right, margin_left
+            ).triu(0)
+
+            margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+            margin.masked_fill_(~margin_mask, 0)
+            margin = margin.max()
+
+            distance = distance - margin
+
+        return distance, height
+
+    def generate_mask(self, x, distance, height, n_cntxt_layers=0):
+        """Compute head and cibling distribution for each token."""
+
+        bsz, length = x.size()
+
+        eye = torch.eye(length, device=x.device, dtype=torch.bool)
+        eye = eye[None, :, :].expand((bsz, -1, -1))
+
+        block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers)
+        head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers)
+        head = torch.einsum("blij,bijh->blh", block_p, head_p)
+        head = head.masked_fill(eye, 0)
+        child = head.transpose(1, 2)
+        cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+        rel_list = []
+        if "head" in self.config.relations:
+            rel_list.append(head)
+        if "child" in self.config.relations:
+            rel_list.append(child)
+        if "cibling" in self.config.relations:
+            rel_list.append(cibling)
+
+        rel = torch.stack(rel_list, dim=1)
+
+        if n_cntxt_layers > 0:
+            if n_cntxt_layers == 1:
+                rel_weight = self.rel_weight_1
+            elif n_cntxt_layers == 2:
+                rel_weight = self.rel_weight_2
+        else:
+            rel_weight = self.rel_weight
+
+        dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel)
+        
+        if n_cntxt_layers == 1:
+            num_layers = self.cntxt_layers_2.config.num_hidden_layers
+        else:
+            num_layers = self.roberta.config.num_hidden_layers
+            
+        att_mask = dep.reshape(
+            num_layers,
+            bsz,
+            self.config.num_attention_heads,
+            length,
+            length,
+        )
+
+        return att_mask, cibling, head, block
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        kwargs (`Dict[str, any]`, optional, defaults to *{}*):
+            Used to hide legacy arguments that have been deprecated.
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        
+        if self.config.n_cntxt_layers > 0:
+            cntxt_outputs = self.cntxt_layers(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict)
+            
+        
+        if self.config.n_cntxt_layers_2 > 0:
+            distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1)
+            att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1)
+            
+            cntxt_outputs_2 = self.cntxt_layers_2(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            parser_att_mask=att_mask_1)
+            
+            sequence_output = cntxt_outputs_2[0]
+            
+            distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2)
+            att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2)
+
+        elif self.config.n_cntxt_layers > 0:
+            distance, height = self.parse(input_ids, cntxt_outputs[0])
+            att_mask, _, _, _ = self.generate_mask(input_ids, distance, height)
+        else:
+            distance, height = self.parse(input_ids)
+            att_mask, _, _, _ = self.generate_mask(input_ids, distance, height)
+
+        outputs = self.roberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            parser_att_mask=att_mask,
+        )
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return (
+                ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+            )
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class RobertaLMHead(nn.Module):
+    """Roberta Head for masked language modeling."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+        self.decoder.bias = self.bias
+
+    def forward(self, features, **kwargs):
+        x = self.dense(features)
+        x = gelu(x)
+        x = self.layer_norm(x)
+
+        # project back to size of vocabulary with bias
+        x = self.decoder(x)
+
+        return x
+
+    def _tie_weights(self):
+        # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
+        self.bias = self.decoder.bias
+
+
+class StructRobertaForSequenceClassification(RobertaPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        if config.n_cntxt_layers > 0:
+            config_cntxt = copy.deepcopy(config)
+            config_cntxt.num_hidden_layers = config.n_cntxt_layers
+            
+            self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False)
+        
+        if config.n_cntxt_layers_2 > 0:
+            self.parser_layers_1 = nn.ModuleList(
+                [
+                    nn.Sequential(
+                        Conv1d(config.hidden_size, config.conv_size),
+                        nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                        nn.Tanh(),
+                    )
+                    for i in range(int(config.n_parser_layers/2))
+                ]
+            )
+
+            self.distance_ff_1 = nn.Sequential(
+                Conv1d(config.hidden_size, 2),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            self.height_ff_1 = nn.Sequential(
+                nn.Linear(config.hidden_size, config.hidden_size),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            n_rel = len(config.relations)
+            self._rel_weight_1 = nn.Parameter(
+                torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel))
+            )
+            self._rel_weight_1.data.normal_(0, 0.1)
+
+            self._scaler_1 = nn.Parameter(torch.zeros(2))
+            
+            config_cntxt_2 = copy.deepcopy(config)
+            config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2
+            
+            self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False)
+            
+            
+            self.parser_layers_2 = nn.ModuleList(
+                [
+                    nn.Sequential(
+                        Conv1d(config.hidden_size, config.conv_size),
+                        nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                        nn.Tanh(),
+                    )
+                    for i in range(int(config.n_parser_layers/2))
+                ]
+            )
+
+            self.distance_ff_2 = nn.Sequential(
+                Conv1d(config.hidden_size, 2),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            self.height_ff_2 = nn.Sequential(
+                nn.Linear(config.hidden_size, config.hidden_size),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            n_rel = len(config.relations)
+            self._rel_weight_2 = nn.Parameter(
+                torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel))
+            )
+            self._rel_weight_2.data.normal_(0, 0.1)
+
+            self._scaler_2 = nn.Parameter(torch.zeros(2))
+            
+        else:
+            self.parser_layers = nn.ModuleList(
+                [
+                    nn.Sequential(
+                        Conv1d(config.hidden_size, config.conv_size),
+                        nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                        nn.Tanh(),
+                    )
+                    for i in range(config.n_parser_layers)
+                ]
+            )
+
+            self.distance_ff = nn.Sequential(
+                Conv1d(config.hidden_size, 2),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            self.height_ff = nn.Sequential(
+                nn.Linear(config.hidden_size, config.hidden_size),
+                nn.LayerNorm(config.hidden_size, elementwise_affine=False),
+                nn.Tanh(),
+                nn.Linear(config.hidden_size, 1),
+            )
+
+            n_rel = len(config.relations)
+            self._rel_weight = nn.Parameter(
+                torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel))
+            )
+            self._rel_weight.data.normal_(0, 0.1)
+
+            self._scaler = nn.Parameter(torch.zeros(2))
+
+        self.roberta = RobertaModel(config, add_pooling_layer=False)
+        
+        if config.n_cntxt_layers > 0:
+            self.cntxt_layers.embeddings = self.roberta.embeddings
+        if config.n_cntxt_layers_2 > 0:
+            self.cntxt_layers_2.embeddings = self.roberta.embeddings
+        
+        
+        self.pad = config.pad_token_id
+        self.classifier = RobertaClassificationHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    
+    @property
+    def scaler(self):
+        return self._scaler.exp()
+    
+    @property
+    def scaler_1(self):
+        return self._scaler_1.exp()
+    
+    @property
+    def scaler_2(self):
+        return self._scaler_2.exp()
+
+    @property
+    def rel_weight(self):
+        if self.config.weight_act == "sigmoid":
+            return torch.sigmoid(self._rel_weight)
+        elif self.config.weight_act == "softmax":
+            return torch.softmax(self._rel_weight, dim=-1)
+        
+    @property
+    def rel_weight_1(self):
+        if self.config.weight_act == "sigmoid":
+            return torch.sigmoid(self._rel_weight_1)
+        elif self.config.weight_act == "softmax":
+            return torch.softmax(self._rel_weight_1, dim=-1)
+
+    
+    @property
+    def rel_weight_2(self):
+        if self.config.weight_act == "sigmoid":
+            return torch.sigmoid(self._rel_weight_2)
+        elif self.config.weight_act == "softmax":
+            return torch.softmax(self._rel_weight_2, dim=-1)
+
+
+    def compute_block(self, distance, height, n_cntxt_layers=0):
+        """Compute constituents from distance and height."""
+
+        if n_cntxt_layers>0:
+            if n_cntxt_layers == 1:
+              beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0]
+            elif n_cntxt_layers == 2:
+              beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0]
+        else:
+            beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0]
+
+        gamma = torch.sigmoid(-beta_logits)
+        ones = torch.ones_like(gamma)
+
+        block_mask_left = cummin(
+            gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1
+        )
+        block_mask_left = block_mask_left - F.pad(
+            block_mask_left[:, :, :-1], (1, 0), value=0
+        )
+        block_mask_left.tril_(0)
+
+        block_mask_right = cummin(
+            gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1
+        )
+        block_mask_right = block_mask_right - F.pad(
+            block_mask_right[:, :, 1:], (0, 1), value=0
+        )
+        block_mask_right.triu_(0)
+
+        block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :]
+        block = cumsum(block_mask_left).tril(0) + cumsum(
+            block_mask_right, reverse=True
+        ).triu(1)
+
+        return block_p, block
+
+    def compute_head(self, height, n_cntxt_layers=0):
+        """Estimate head for each constituent."""
+
+        _, length = height.size()
+        if n_cntxt_layers>0:
+          if n_cntxt_layers == 1:
+              head_logits = height * self.scaler_1[1]
+          elif n_cntxt_layers == 2:
+              head_logits = height * self.scaler_2[1]  
+        else:
+            head_logits = height * self.scaler[1]
+        index = torch.arange(length, device=height.device)
+
+        mask = (index[:, None, None] <= index[None, None, :]) * (
+            index[None, None, :] <= index[None, :, None]
+        )
+        head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1)
+        head_logits.masked_fill_(~mask[None, :, :, :], -1e9)
+
+        head_p = torch.softmax(head_logits, dim=-1)
+
+        return head_p
+
+    def parse(self, x, embs=None, n_cntxt_layers=0):
+        """Parse input sentence.
+
+        Args:
+        x: input tokens (required).
+        pos: position for each token (optional).
+        Returns:
+        distance: syntactic distance
+        height: syntactic height
+        """
+
+        mask = x != self.pad
+        mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0)
+
+        if embs is None:
+            h = self.roberta.embeddings(x)
+        else:
+            h = embs
+        
+        if n_cntxt_layers > 0:
+            if n_cntxt_layers == 1:
+                parser_layers = self.parser_layers_1
+                height_ff = self.height_ff_1
+                distance_ff = self.distance_ff_1
+            elif n_cntxt_layers == 2:
+                parser_layers = self.parser_layers_2
+                height_ff = self.height_ff_2
+                distance_ff = self.distance_ff_2
+            for i in range(int(self.config.n_parser_layers/2)):
+                h = h.masked_fill(~mask[:, :, None], 0)
+                h = parser_layers[i](h)
+
+            height = height_ff(h).squeeze(-1)
+            height.masked_fill_(~mask, -1e9)
+
+            distance = distance_ff(h).squeeze(-1)
+            distance.masked_fill_(~mask_shifted, 1e9)
+
+            # Calbrating the distance and height to the same level
+            length = distance.size(1)
+            height_max = height[:, None, :].expand(-1, length, -1)
+            height_max = torch.cummax(
+                height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1
+            )[0].triu(0)
+
+            margin_left = torch.relu(
+                F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max
+            )
+            margin_right = torch.relu(distance[:, None, :] - height_max)
+            margin = torch.where(
+                margin_left > margin_right, margin_right, margin_left
+            ).triu(0)
+
+            margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+            margin.masked_fill_(~margin_mask, 0)
+            margin = margin.max()
+
+            distance = distance - margin
+        else:
+            for i in range(self.config.n_parser_layers):
+                h = h.masked_fill(~mask[:, :, None], 0)
+                h = self.parser_layers[i](h)
+
+            height = self.height_ff(h).squeeze(-1)
+            height.masked_fill_(~mask, -1e9)
+
+            distance = self.distance_ff(h).squeeze(-1)
+            distance.masked_fill_(~mask_shifted, 1e9)
+
+            # Calbrating the distance and height to the same level
+            length = distance.size(1)
+            height_max = height[:, None, :].expand(-1, length, -1)
+            height_max = torch.cummax(
+                height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1
+            )[0].triu(0)
+
+            margin_left = torch.relu(
+                F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max
+            )
+            margin_right = torch.relu(distance[:, None, :] - height_max)
+            margin = torch.where(
+                margin_left > margin_right, margin_right, margin_left
+            ).triu(0)
+
+            margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1)
+            margin.masked_fill_(~margin_mask, 0)
+            margin = margin.max()
+
+            distance = distance - margin
+
+        return distance, height
+
+    def generate_mask(self, x, distance, height, n_cntxt_layers=0):
+        """Compute head and cibling distribution for each token."""
+
+        bsz, length = x.size()
+
+        eye = torch.eye(length, device=x.device, dtype=torch.bool)
+        eye = eye[None, :, :].expand((bsz, -1, -1))
+
+        block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers)
+        head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers)
+        head = torch.einsum("blij,bijh->blh", block_p, head_p)
+        head = head.masked_fill(eye, 0)
+        child = head.transpose(1, 2)
+        cibling = torch.bmm(head, child).masked_fill(eye, 0)
+
+        rel_list = []
+        if "head" in self.config.relations:
+            rel_list.append(head)
+        if "child" in self.config.relations:
+            rel_list.append(child)
+        if "cibling" in self.config.relations:
+            rel_list.append(cibling)
+
+        rel = torch.stack(rel_list, dim=1)
+
+        if n_cntxt_layers > 0:
+            if n_cntxt_layers == 1:
+                rel_weight = self.rel_weight_1
+            elif n_cntxt_layers == 2:
+                rel_weight = self.rel_weight_2
+        else:
+            rel_weight = self.rel_weight
+
+        dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel)
+        
+        if n_cntxt_layers == 1:
+            num_layers = self.cntxt_layers_2.config.num_hidden_layers
+        else:
+            num_layers = self.roberta.config.num_hidden_layers
+            
+        att_mask = dep.reshape(
+            num_layers,
+            bsz,
+            self.config.num_attention_heads,
+            length,
+            length,
+        )
+
+        return att_mask, cibling, head, block
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        if self.config.n_cntxt_layers > 0:
+            cntxt_outputs = self.cntxt_layers(
+                input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                position_ids=position_ids,
+                head_mask=head_mask,
+                inputs_embeds=inputs_embeds,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict)
+            
+        
+        if self.config.n_cntxt_layers_2 > 0:
+            distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1)
+            att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1)
+            
+            cntxt_outputs_2 = self.cntxt_layers_2(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            parser_att_mask=att_mask_1)
+            
+            sequence_output = cntxt_outputs_2[0]
+            
+            distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2)
+            att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2)
+
+        elif self.config.n_cntxt_layers > 0:
+            distance, height = self.parse(input_ids, cntxt_outputs[0])
+            att_mask, _, _, _ = self.generate_mask(input_ids, distance, height)
+        else:
+            distance, height = self.parse(input_ids)
+            att_mask, _, _, _ = self.generate_mask(input_ids, distance, height)
+
+        outputs = self.roberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            parser_att_mask=att_mask,
+        )
+        sequence_output = outputs[0]
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class RobertaClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        classifier_dropout = (
+            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+        return x
+
+
+def create_position_ids_from_input_ids(
+    input_ids, padding_idx, past_key_values_length=0
+):
+    """
+    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+    are ignored. This is modified from fairseq's `utils.make_positions`.
+
+    Args:
+        x: torch.Tensor x:
+
+    Returns: torch.Tensor
+    """
+    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+    mask = input_ids.ne(padding_idx).int()
+    incremental_indices = (
+        torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length
+    ) * mask
+    return incremental_indices.long() + padding_idx
+
+
+def cumprod(x, reverse=False, exclusive=False):
+    """cumulative product."""
+    if reverse:
+        x = x.flip([-1])
+
+    if exclusive:
+        x = F.pad(x[:, :, :-1], (1, 0), value=1)
+
+    cx = x.cumprod(-1)
+
+    if reverse:
+        cx = cx.flip([-1])
+    return cx
+
+
+def cumsum(x, reverse=False, exclusive=False):
+    """cumulative sum."""
+    bsz, _, length = x.size()
+    device = x.device
+    if reverse:
+        if exclusive:
+            w = torch.ones([bsz, length, length], device=device).tril(-1)
+        else:
+            w = torch.ones([bsz, length, length], device=device).tril(0)
+        cx = torch.bmm(x, w)
+    else:
+        if exclusive:
+            w = torch.ones([bsz, length, length], device=device).triu(1)
+        else:
+            w = torch.ones([bsz, length, length], device=device).triu(0)
+        cx = torch.bmm(x, w)
+    return cx
+
+
+def cummin(x, reverse=False, exclusive=False, max_value=1e9):
+    """cumulative min."""
+    if reverse:
+        if exclusive:
+            x = F.pad(x[:, :, 1:], (0, 1), value=max_value)
+        x = x.flip([-1]).cummin(-1)[0].flip([-1])
+    else:
+        if exclusive:
+            x = F.pad(x[:, :, :-1], (1, 0), value=max_value)
+        x = x.cummin(-1)[0]
+    return x

pytorch_model.bin

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+oid sha256:818c667dfd50dc5dd559e30d4f79a648b8e2334ccdcc2fc2cffbea6b50a10ff4
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