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 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