# coding=utf-8 # Copyright 2020 Microsoft and the Hugging Face Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch DeBERTa-v2 model.""" from collections.abc import Sequence from typing import Optional, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss from ...activations import ACT2FN from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import ( BaseModelOutput, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import auto_docstring, logging from .configuration_deberta_v2 import DebertaV2Config logger = logging.get_logger(__name__) # Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm class DebertaV2SelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states @torch.jit.script def make_log_bucket_position(relative_pos, bucket_size: int, max_position: int): sign = torch.sign(relative_pos) mid = bucket_size // 2 abs_pos = torch.where( (relative_pos < mid) & (relative_pos > -mid), torch.tensor(mid - 1).type_as(relative_pos), torch.abs(relative_pos), ) log_pos = ( torch.ceil(torch.log(abs_pos / mid) / torch.log(torch.tensor((max_position - 1) / mid)) * (mid - 1)) + mid ) bucket_pos = torch.where(abs_pos <= mid, relative_pos.type_as(log_pos), log_pos * sign) return bucket_pos def build_relative_position(query_layer, key_layer, bucket_size: int = -1, max_position: int = -1): """ Build relative position according to the query and key We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q - P_k\\) Args: query_size (int): the length of query key_size (int): the length of key bucket_size (int): the size of position bucket max_position (int): the maximum allowed absolute position device (`torch.device`): the device on which tensors will be created. Return: `torch.LongTensor`: A tensor with shape [1, query_size, key_size] """ query_size = query_layer.size(-2) key_size = key_layer.size(-2) q_ids = torch.arange(query_size, dtype=torch.long, device=query_layer.device) k_ids = torch.arange(key_size, dtype=torch.long, device=key_layer.device) rel_pos_ids = q_ids[:, None] - k_ids[None, :] if bucket_size > 0 and max_position > 0: rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position) rel_pos_ids = rel_pos_ids.to(torch.long) rel_pos_ids = rel_pos_ids[:query_size, :] rel_pos_ids = rel_pos_ids.unsqueeze(0) return rel_pos_ids @torch.jit.script # Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos): return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)]) @torch.jit.script # Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand def p2c_dynamic_expand(c2p_pos, query_layer, key_layer): return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)]) @torch.jit.script # Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand def pos_dynamic_expand(pos_index, p2c_att, key_layer): return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2))) @torch.jit.script def scaled_size_sqrt(query_layer: torch.Tensor, scale_factor: int): return torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor) @torch.jit.script def build_rpos(query_layer, key_layer, relative_pos, position_buckets: int, max_relative_positions: int): if key_layer.size(-2) != query_layer.size(-2): return build_relative_position( key_layer, key_layer, bucket_size=position_buckets, max_position=max_relative_positions, ) else: return relative_pos class DisentangledSelfAttention(nn.Module): """ Disentangled self-attention module Parameters: config (`DebertaV2Config`): A model config class instance with the configuration to build a new model. The schema is similar to *BertConfig*, for more details, please refer [`DebertaV2Config`] """ def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0: 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 _attention_head_size = config.hidden_size // config.num_attention_heads self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) self.share_att_key = getattr(config, "share_att_key", False) self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else [] self.relative_attention = getattr(config, "relative_attention", False) if self.relative_attention: self.position_buckets = getattr(config, "position_buckets", -1) self.max_relative_positions = getattr(config, "max_relative_positions", -1) if self.max_relative_positions < 1: self.max_relative_positions = config.max_position_embeddings self.pos_ebd_size = self.max_relative_positions if self.position_buckets > 0: self.pos_ebd_size = self.position_buckets self.pos_dropout = nn.Dropout(config.hidden_dropout_prob) if not self.share_att_key: if "c2p" in self.pos_att_type: self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) if "p2c" in self.pos_att_type: self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x, attention_heads) -> torch.Tensor: new_x_shape = x.size()[:-1] + (attention_heads, -1) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1)) def forward( self, hidden_states, attention_mask, output_attentions=False, query_states=None, relative_pos=None, rel_embeddings=None, ): """ Call the module Args: hidden_states (`torch.FloatTensor`): Input states to the module usually the output from previous layer, it will be the Q,K and V in *Attention(Q,K,V)* attention_mask (`torch.BoolTensor`): An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the batch size, *N* is the maximum sequence length in which element [i,j] = *1* means the *i* th token in the input can attend to the *j* th token. output_attentions (`bool`, *optional*): Whether return the attention matrix. query_states (`torch.FloatTensor`, *optional*): The *Q* state in *Attention(Q,K,V)*. relative_pos (`torch.LongTensor`): The relative position encoding between the tokens in the sequence. It's of shape [*B*, *N*, *N*] with values ranging in [*-max_relative_positions*, *max_relative_positions*]. rel_embeddings (`torch.FloatTensor`): The embedding of relative distances. It's a tensor of shape [\\(2 \\times \\text{max_relative_positions}\\), *hidden_size*]. """ if query_states is None: query_states = hidden_states query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads) key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads) value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads) rel_att = None # Take the dot product between "query" and "key" to get the raw attention scores. scale_factor = 1 if "c2p" in self.pos_att_type: scale_factor += 1 if "p2c" in self.pos_att_type: scale_factor += 1 scale = scaled_size_sqrt(query_layer, scale_factor) attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2) / scale.to(dtype=query_layer.dtype)) if self.relative_attention: rel_embeddings = self.pos_dropout(rel_embeddings) rel_att = self.disentangled_attention_bias( query_layer, key_layer, relative_pos, rel_embeddings, scale_factor ) if rel_att is not None: attention_scores = attention_scores + rel_att attention_scores = attention_scores attention_scores = attention_scores.view( -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1) ) attention_mask = attention_mask.bool() attention_scores = attention_scores.masked_fill(~(attention_mask), torch.finfo(query_layer.dtype).min) # bsz x height x length x dimension attention_probs = nn.functional.softmax(attention_scores, dim=-1) attention_probs = self.dropout(attention_probs) context_layer = torch.bmm( attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer ) context_layer = ( context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1)) .permute(0, 2, 1, 3) .contiguous() ) new_context_layer_shape = context_layer.size()[:-2] + (-1,) context_layer = context_layer.view(new_context_layer_shape) if not output_attentions: return (context_layer, None) return (context_layer, attention_probs) def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor): if relative_pos is None: relative_pos = build_relative_position( query_layer, key_layer, bucket_size=self.position_buckets, max_position=self.max_relative_positions, ) if relative_pos.dim() == 2: relative_pos = relative_pos.unsqueeze(0).unsqueeze(0) elif relative_pos.dim() == 3: relative_pos = relative_pos.unsqueeze(1) # bsz x height x query x key elif relative_pos.dim() != 4: raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}") att_span = self.pos_ebd_size relative_pos = relative_pos.to(device=query_layer.device, dtype=torch.long) rel_embeddings = rel_embeddings[0 : att_span * 2, :].unsqueeze(0) if self.share_att_key: pos_query_layer = self.transpose_for_scores( self.query_proj(rel_embeddings), self.num_attention_heads ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat( query_layer.size(0) // self.num_attention_heads, 1, 1 ) else: if "c2p" in self.pos_att_type: pos_key_layer = self.transpose_for_scores( self.pos_key_proj(rel_embeddings), self.num_attention_heads ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) if "p2c" in self.pos_att_type: pos_query_layer = self.transpose_for_scores( self.pos_query_proj(rel_embeddings), self.num_attention_heads ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) score = 0 # content->position if "c2p" in self.pos_att_type: scale = scaled_size_sqrt(pos_key_layer, scale_factor) c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2)) c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1) c2p_att = torch.gather( c2p_att, dim=-1, index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]), ) score += c2p_att / scale.to(dtype=c2p_att.dtype) # position->content if "p2c" in self.pos_att_type: scale = scaled_size_sqrt(pos_query_layer, scale_factor) r_pos = build_rpos( query_layer, key_layer, relative_pos, self.max_relative_positions, self.position_buckets, ) p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1) p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2)) p2c_att = torch.gather( p2c_att, dim=-1, index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]), ).transpose(-1, -2) score += p2c_att / scale.to(dtype=p2c_att.dtype) return score # Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2 class DebertaV2Attention(nn.Module): def __init__(self, config): super().__init__() self.self = DisentangledSelfAttention(config) self.output = DebertaV2SelfOutput(config) self.config = config def forward( self, hidden_states, attention_mask, output_attentions: bool = False, query_states=None, relative_pos=None, rel_embeddings=None, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: self_output, att_matrix = self.self( hidden_states, attention_mask, output_attentions, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, ) if query_states is None: query_states = hidden_states attention_output = self.output(self_output, query_states) if output_attentions: return (attention_output, att_matrix) else: return (attention_output, None) # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2 class DebertaV2Intermediate(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.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm class DebertaV2Output(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def forward(self, hidden_states, input_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.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2 class DebertaV2Layer(GradientCheckpointingLayer): def __init__(self, config): super().__init__() self.attention = DebertaV2Attention(config) self.intermediate = DebertaV2Intermediate(config) self.output = DebertaV2Output(config) def forward( self, hidden_states, attention_mask, query_states=None, relative_pos=None, rel_embeddings=None, output_attentions: bool = False, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: attention_output, att_matrix = self.attention( hidden_states, attention_mask, output_attentions=output_attentions, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, ) intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) if output_attentions: return (layer_output, att_matrix) else: return (layer_output, None) class ConvLayer(nn.Module): def __init__(self, config): super().__init__() kernel_size = getattr(config, "conv_kernel_size", 3) groups = getattr(config, "conv_groups", 1) self.conv_act = getattr(config, "conv_act", "tanh") self.conv = nn.Conv1d( config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups ) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def forward(self, hidden_states, residual_states, input_mask): out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous() rmask = (1 - input_mask).bool() out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0) out = ACT2FN[self.conv_act](self.dropout(out)) layer_norm_input = residual_states + out output = self.LayerNorm(layer_norm_input).to(layer_norm_input) if input_mask is None: output_states = output else: if input_mask.dim() != layer_norm_input.dim(): if input_mask.dim() == 4: input_mask = input_mask.squeeze(1).squeeze(1) input_mask = input_mask.unsqueeze(2) input_mask = input_mask.to(output.dtype) output_states = output * input_mask return output_states # Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm,Deberta->DebertaV2 class DebertaV2Embeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() pad_token_id = getattr(config, "pad_token_id", 0) self.embedding_size = getattr(config, "embedding_size", config.hidden_size) self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id) self.position_biased_input = getattr(config, "position_biased_input", True) if not self.position_biased_input: self.position_embeddings = None else: self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size) if config.type_vocab_size > 0: self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size) else: self.token_type_embeddings = None if self.embedding_size != config.hidden_size: self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False) else: self.embed_proj = None self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: 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) if self.position_embeddings is not None: position_embeddings = self.position_embeddings(position_ids.long()) else: position_embeddings = torch.zeros_like(inputs_embeds) embeddings = inputs_embeds if self.position_biased_input: embeddings = embeddings + position_embeddings if self.token_type_embeddings is not None: token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = embeddings + token_type_embeddings if self.embed_proj is not None: embeddings = self.embed_proj(embeddings) embeddings = self.LayerNorm(embeddings) if mask is not None: if mask.dim() != embeddings.dim(): if mask.dim() == 4: mask = mask.squeeze(1).squeeze(1) mask = mask.unsqueeze(2) mask = mask.to(embeddings.dtype) embeddings = embeddings * mask embeddings = self.dropout(embeddings) return embeddings class DebertaV2Encoder(nn.Module): """Modified BertEncoder with relative position bias support""" def __init__(self, config): super().__init__() self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)]) self.relative_attention = getattr(config, "relative_attention", False) if self.relative_attention: self.max_relative_positions = getattr(config, "max_relative_positions", -1) if self.max_relative_positions < 1: self.max_relative_positions = config.max_position_embeddings self.position_buckets = getattr(config, "position_buckets", -1) pos_ebd_size = self.max_relative_positions * 2 if self.position_buckets > 0: pos_ebd_size = self.position_buckets * 2 self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size) self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("|")] if "layer_norm" in self.norm_rel_ebd: self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True) self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None self.gradient_checkpointing = False def get_rel_embedding(self): rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd): rel_embeddings = self.LayerNorm(rel_embeddings) return rel_embeddings def get_attention_mask(self, attention_mask): if attention_mask.dim() <= 2: extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1) elif attention_mask.dim() == 3: attention_mask = attention_mask.unsqueeze(1) return attention_mask def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None): if self.relative_attention and relative_pos is None: if query_states is not None: relative_pos = build_relative_position( query_states, hidden_states, bucket_size=self.position_buckets, max_position=self.max_relative_positions, ) else: relative_pos = build_relative_position( hidden_states, hidden_states, bucket_size=self.position_buckets, max_position=self.max_relative_positions, ) return relative_pos def forward( self, hidden_states, attention_mask, output_hidden_states=True, output_attentions=False, query_states=None, relative_pos=None, return_dict=True, ): if attention_mask.dim() <= 2: input_mask = attention_mask else: input_mask = attention_mask.sum(-2) > 0 attention_mask = self.get_attention_mask(attention_mask) relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos) all_hidden_states: Optional[tuple[torch.Tensor]] = (hidden_states,) if output_hidden_states else None all_attentions = () if output_attentions else None next_kv = hidden_states rel_embeddings = self.get_rel_embedding() for i, layer_module in enumerate(self.layer): output_states, attn_weights = layer_module( next_kv, attention_mask, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, output_attentions=output_attentions, ) if output_attentions: all_attentions = all_attentions + (attn_weights,) if i == 0 and self.conv is not None: output_states = self.conv(hidden_states, output_states, input_mask) if output_hidden_states: all_hidden_states = all_hidden_states + (output_states,) if query_states is not None: query_states = output_states if isinstance(hidden_states, Sequence): next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None else: next_kv = output_states if not return_dict: return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions ) @auto_docstring class DebertaV2PreTrainedModel(PreTrainedModel): config: DebertaV2Config base_model_prefix = "deberta" _keys_to_ignore_on_load_unexpected = ["position_embeddings"] supports_gradient_checkpointing = True 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): module.weight.data.fill_(1.0) module.bias.data.zero_() elif isinstance(module, (LegacyDebertaV2LMPredictionHead, DebertaV2LMPredictionHead)): module.bias.data.zero_() @auto_docstring # Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2 class DebertaV2Model(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) self.embeddings = DebertaV2Embeddings(config) self.encoder = DebertaV2Encoder(config) self.z_steps = 0 self.config = config # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, new_embeddings): self.embeddings.word_embeddings = new_embeddings 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 """ raise NotImplementedError("The prune function is not implemented in DeBERTa model.") @auto_docstring 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, inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutput]: 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 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: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) 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") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, mask=attention_mask, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, attention_mask, output_hidden_states=True, output_attentions=output_attentions, return_dict=return_dict, ) encoded_layers = encoder_outputs[1] if self.z_steps > 1: hidden_states = encoded_layers[-2] layers = [self.encoder.layer[-1] for _ in range(self.z_steps)] query_states = encoded_layers[-1] rel_embeddings = self.encoder.get_rel_embedding() attention_mask = self.encoder.get_attention_mask(attention_mask) rel_pos = self.encoder.get_rel_pos(embedding_output) for layer in layers[1:]: query_states = layer( hidden_states, attention_mask, output_attentions=False, query_states=query_states, relative_pos=rel_pos, rel_embeddings=rel_embeddings, ) encoded_layers.append(query_states) sequence_output = encoded_layers[-1] if not return_dict: return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :] return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states if output_hidden_states else None, attentions=encoder_outputs.attentions, ) # Copied from transformers.models.deberta.modeling_deberta.LegacyDebertaPredictionHeadTransform with Deberta->DebertaV2 class LegacyDebertaV2PredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.embedding_size = getattr(config, "embedding_size", config.hidden_size) self.dense = nn.Linear(config.hidden_size, self.embedding_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(self.embedding_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class LegacyDebertaV2LMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = LegacyDebertaV2PredictionHeadTransform(config) self.embedding_size = getattr(config, "embedding_size", config.hidden_size) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(self.embedding_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def _tie_weights(self): self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class LegacyDebertaV2OnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = LegacyDebertaV2LMPredictionHead(config) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores class DebertaV2LMPredictionHead(nn.Module): """https://github.com/microsoft/DeBERTa/blob/master/DeBERTa/deberta/bert.py#L270""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=True) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # note that the input embeddings must be passed as an argument def forward(self, hidden_states, word_embeddings): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) hidden_states = torch.matmul(hidden_states, word_embeddings.weight.t()) + self.bias return hidden_states class DebertaV2OnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.lm_head = DebertaV2LMPredictionHead(config) # note that the input embeddings must be passed as an argument def forward(self, sequence_output, word_embeddings): prediction_scores = self.lm_head(sequence_output, word_embeddings) return prediction_scores @auto_docstring class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"] _keys_to_ignore_on_load_unexpected = r"mask_predictions.*" def __init__(self, config): super().__init__(config) self.legacy = config.legacy self.deberta = DebertaV2Model(config) if self.legacy: self.cls = LegacyDebertaV2OnlyMLMHead(config) else: self._tied_weights_keys = ["lm_predictions.lm_head.weight", "deberta.embeddings.word_embeddings.weight"] self.lm_predictions = DebertaV2OnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): if self.legacy: return self.cls.predictions.decoder else: return self.lm_predictions.lm_head.dense def set_output_embeddings(self, new_embeddings): if self.legacy: self.cls.predictions.decoder = new_embeddings self.cls.predictions.bias = new_embeddings.bias else: self.lm_predictions.lm_head.dense = new_embeddings self.lm_predictions.lm_head.bias = new_embeddings.bias @auto_docstring # Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM.forward with Deberta->DebertaV2 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, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = 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]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.deberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] if self.legacy: prediction_scores = self.cls(sequence_output) else: prediction_scores = self.lm_predictions(sequence_output, self.deberta.embeddings.word_embeddings) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[1:] 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, ) # Copied from transformers.models.deberta.modeling_deberta.ContextPooler class ContextPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size) self.dropout = nn.Dropout(config.pooler_dropout) self.config = config def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. context_token = hidden_states[:, 0] context_token = self.dropout(context_token) pooled_output = self.dense(context_token) pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output) return pooled_output @property def output_dim(self): return self.config.hidden_size @auto_docstring( custom_intro=""" DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """ ) class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) num_labels = getattr(config, "num_labels", 2) self.num_labels = num_labels self.deberta = DebertaV2Model(config) self.pooler = ContextPooler(config) output_dim = self.pooler.output_dim self.classifier = nn.Linear(output_dim, num_labels) drop_out = getattr(config, "cls_dropout", None) drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out self.dropout = nn.Dropout(drop_out) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.deberta.get_input_embeddings() def set_input_embeddings(self, new_embeddings): self.deberta.set_input_embeddings(new_embeddings) @auto_docstring # Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification.forward with Deberta->DebertaV2 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, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = 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 outputs = self.deberta( input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) encoder_layer = outputs[0] pooled_output = self.pooler(encoder_layer) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: # regression task loss_fn = nn.MSELoss() logits = logits.view(-1).to(labels.dtype) loss = loss_fn(logits, labels.view(-1)) elif labels.dim() == 1 or labels.size(-1) == 1: label_index = (labels >= 0).nonzero() labels = labels.long() if label_index.size(0) > 0: labeled_logits = torch.gather( logits, 0, label_index.expand(label_index.size(0), logits.size(1)) ) labels = torch.gather(labels, 0, label_index.view(-1)) loss_fct = CrossEntropyLoss() loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1)) else: loss = torch.tensor(0).to(logits) else: log_softmax = nn.LogSoftmax(-1) loss = -((log_softmax(logits) * labels).sum(-1)).mean() elif 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[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions ) @auto_docstring # Copied from transformers.models.deberta.modeling_deberta.DebertaForTokenClassification with Deberta->DebertaV2 class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.deberta = DebertaV2Model(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @auto_docstring 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, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.deberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions ) @auto_docstring class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.deberta = DebertaV2Model(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @auto_docstring # Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering.forward with Deberta->DebertaV2 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, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, QuestionAnsweringModelOutput]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.deberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[1:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @auto_docstring class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) num_labels = getattr(config, "num_labels", 2) self.num_labels = num_labels self.deberta = DebertaV2Model(config) self.pooler = ContextPooler(config) output_dim = self.pooler.output_dim self.classifier = nn.Linear(output_dim, 1) drop_out = getattr(config, "cls_dropout", None) drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out self.dropout = nn.Dropout(drop_out) self.init_weights() def get_input_embeddings(self): return self.deberta.get_input_embeddings() def set_input_embeddings(self, new_embeddings): self.deberta.set_input_embeddings(new_embeddings) @auto_docstring 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, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.deberta( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) encoder_layer = outputs[0] pooled_output = self.pooler(encoder_layer) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = [ "DebertaV2ForMaskedLM", "DebertaV2ForMultipleChoice", "DebertaV2ForQuestionAnswering", "DebertaV2ForSequenceClassification", "DebertaV2ForTokenClassification", "DebertaV2Model", "DebertaV2PreTrainedModel", ]