# 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. """ import math from collections.abc import Sequence from typing import Tuple, Optional import clip import numpy as np import torch from torch import _softmax_backward_data, nn from torch.nn import CrossEntropyLoss, LayerNorm from .adapter import Adapter from .moe import MoE from transformers.activations import ACT2FN from transformers.modeling_outputs import ModelOutput from transformers.modeling_utils import PreTrainedModel from transformers import DebertaV2Config, DebertaV2ForSequenceClassification from .evl import EVLTransformer, recursive_gumbel_softmax from transformers import pytorch_utils _CONFIG_FOR_DOC = "DebertaV2Config" _TOKENIZER_FOR_DOC = "DebertaV2Tokenizer" _CHECKPOINT_FOR_DOC = "microsoft/deberta-v2-xlarge" DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/deberta-v2-xlarge", "microsoft/deberta-v2-xxlarge", "microsoft/deberta-v2-xlarge-mnli", "microsoft/deberta-v2-xxlarge-mnli", ] class MaskedLMOutput(ModelOutput): """ Base class for masked language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Masked language modeling (MLM) loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None loss_moe: Optional[torch.FloatTensor] = None loads: Optional[torch.FloatTensor] = None embeddings: Optional[torch.FloatTensor] = None class BaseModelOutput(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None position_embeddings: torch.FloatTensor = None attention_mask: torch.BoolTensor = None loss_moe: torch.FloatTensor = None video_g: torch.FloatTensor = None loads: torch.LongTensor = None embeddings: torch.FloatTensor = None # 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 = StableDropout(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 # Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2 class XSoftmax(torch.autograd.Function): """ Masked Softmax which is optimized for saving memory Args: input (:obj:`torch.tensor`): The input tensor that will apply softmax. mask (:obj:`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation. dim (int): The dimension that will apply softmax Example:: import torch from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax # Make a tensor x = torch.randn([4,20,100]) # Create a mask mask = (x>0).int() y = XSoftmax.apply(x, mask, dim=-1) """ @staticmethod def forward(self, input, mask, dim): self.dim = dim rmask = ~(mask.bool()) output = input.masked_fill(rmask, float("-inf")) output = torch.softmax(output, self.dim) output.masked_fill_(rmask, 0) self.save_for_backward(output) return output @staticmethod def backward(self, grad_output): (output,) = self.saved_tensors inputGrad = _softmax_backward_data(grad_output, output, self.dim, output.dtype) return inputGrad, None, None # Copied from transformers.models.deberta.modeling_deberta.DropoutContext class DropoutContext(object): def __init__(self): self.dropout = 0 self.mask = None self.scale = 1 self.reuse_mask = True # Copied from transformers.models.deberta.modeling_deberta.get_mask def get_mask(input, local_context): if not isinstance(local_context, DropoutContext): dropout = local_context mask = None else: dropout = local_context.dropout dropout *= local_context.scale mask = local_context.mask if local_context.reuse_mask else None if dropout > 0 and mask is None: mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).bool() if isinstance(local_context, DropoutContext): if local_context.mask is None: local_context.mask = mask return mask, dropout # Copied from transformers.models.deberta.modeling_deberta.XDropout class XDropout(torch.autograd.Function): """Optimized dropout function to save computation and memory by using mask operation instead of multiplication.""" @staticmethod def forward(ctx, input, local_ctx): mask, dropout = get_mask(input, local_ctx) ctx.scale = 1.0 / (1 - dropout) if dropout > 0: ctx.save_for_backward(mask) return input.masked_fill(mask, 0) * ctx.scale else: return input @staticmethod def backward(ctx, grad_output): if ctx.scale > 1: (mask,) = ctx.saved_tensors return grad_output.masked_fill(mask, 0) * ctx.scale, None else: return grad_output, None # Copied from transformers.models.deberta.modeling_deberta.StableDropout class StableDropout(nn.Module): """ Optimized dropout module for stabilizing the training Args: drop_prob (float): the dropout probabilities """ def __init__(self, drop_prob): super().__init__() self.drop_prob = drop_prob self.count = 0 self.context_stack = None def forward(self, x): """ Call the module Args: x (:obj:`torch.tensor`): The input tensor to apply dropout """ if self.training and self.drop_prob > 0: return XDropout.apply(x, self.get_context()) return x def clear_context(self): self.count = 0 self.context_stack = None def init_context(self, reuse_mask=True, scale=1): if self.context_stack is None: self.context_stack = [] self.count = 0 for c in self.context_stack: c.reuse_mask = reuse_mask c.scale = scale def get_context(self): if self.context_stack is not None: if self.count >= len(self.context_stack): self.context_stack.append(DropoutContext()) ctx = self.context_stack[self.count] ctx.dropout = self.drop_prob self.count += 1 return ctx else: return self.drop_prob # Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm class DebertaV2SelfOutput(nn.Module): def __init__(self, config, ds_factor, dropout, add_moe, gating): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = StableDropout(config.hidden_dropout_prob) self.add_moe = add_moe if not self.add_moe and ds_factor: self.adapter = Adapter(ds_factor, config.hidden_size, dropout=dropout) else: self.moe_layer = MoE(ds_factor = ds_factor, moe_input_size=config.hidden_size, dropout=dropout, num_experts=4, top_k=2, gating=gating) def forward(self, hidden_states, input_tensor, temporal_factor = None, train_mode = True): hidden_states = self.dense(hidden_states) if not self.add_moe: hidden_states = self.adapter(hidden_states) else: hidden_states, loss_moe, load = self.moe_layer(temporal_factor, hidden_states, train=train_mode) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) if not self.add_moe: return hidden_states, None, None return hidden_states, loss_moe, load # Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2 class DebertaV2Attention(nn.Module): def __init__(self, config, ds_factor, dropout, add_moe = False, gating='linear'): super().__init__() self.self = DisentangledSelfAttention(config) self.output = DebertaV2SelfOutput(config, ds_factor, dropout, add_moe, gating) self.config = config def forward( self, hidden_states, attention_mask, return_att=False, query_states=None, relative_pos=None, rel_embeddings=None, temporal_factor=None, train_mode=True ): self_output = self.self( hidden_states, attention_mask, return_att, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, ) if return_att: self_output, att_matrix = self_output if query_states is None: query_states = hidden_states attention_output, loss_moe, load = self.output(self_output, query_states, temporal_factor, train_mode) if return_att: return (attention_output, att_matrix, loss_moe) else: return attention_output, loss_moe, load # 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): 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, ds_factor, dropout, add_moe = False, gating='linear',layer_id=0): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = StableDropout(config.hidden_dropout_prob) self.config = config self.ds_factor = ds_factor self.add_moe = add_moe if not self.add_moe and self.ds_factor: self.adapter = Adapter(ds_factor, config.hidden_size, dropout=dropout) elif self.add_moe: self.moe_layer = MoE(ds_factor=ds_factor, moe_input_size=config.hidden_size, dropout=dropout, num_experts=4, top_k=1, gating=gating, layer_id=layer_id) #self.adapter = Adapter(ds_factor, config.hidden_size, dropout=dropout) def forward(self, hidden_states, input_tensor, temporal_factor, train_mode): hidden_states = self.dense(hidden_states) if not self.add_moe and self.ds_factor: hidden_states = self.adapter(hidden_states) elif self.add_moe: hidden_states, loss_moe, load = self.moe_layer(temporal_factor, hidden_states, train=train_mode) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) if not self.add_moe: return hidden_states, None, None return hidden_states, loss_moe, load # Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2 class DebertaV2Layer(nn.Module): def __init__( self, config, ds_factor_attn, ds_factor_ff, dropout, layer_id, ): super().__init__() self.layer_id = layer_id self.add_moe = False #if layer_id >= config.num_hidden_layers - 2: # self.add_moe = True if layer_id < 2: self.add_moe = True self.attention = DebertaV2Attention(config, ds_factor_attn, dropout, False) self.intermediate = DebertaV2Intermediate(config) self.output = DebertaV2Output(config, ds_factor_ff, dropout, self.add_moe, gating="linear", layer_id = layer_id) def forward( self, temporal_factor, hidden_states, attention_mask, return_att=False, query_states=None, relative_pos=None, rel_embeddings=None, train_mode=True, ): attention_output = self.attention( hidden_states, attention_mask, return_att=return_att, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, temporal_factor=temporal_factor, train_mode=train_mode ) if return_att: attention_output, att_matrix, loss_moe_attn = attention_output else: attention_output, loss_moe_attn, load = attention_output intermediate_output = self.intermediate(attention_output) layer_output, loss_moe_ffn, load = self.output(intermediate_output, attention_output, temporal_factor=temporal_factor, train_mode=train_mode) loss_moe = loss_moe_attn if loss_moe_attn else loss_moe_ffn if return_att: return (layer_output, att_matrix) return layer_output, loss_moe, load 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 = StableDropout(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 class DebertaV2Encoder(nn.Module): """Modified BertEncoder with relative position bias support""" def __init__( self, config, ds_factor_attn, ds_factor_ff, dropout, ): super().__init__() self.layer = nn.ModuleList( [ DebertaV2Layer( config, ds_factor_attn, ds_factor_ff, dropout, _, ) 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 ) 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) attention_mask = attention_mask.byte() 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: q = ( query_states.size(-2) if query_states is not None else hidden_states.size(-2) ) relative_pos = build_relative_position( q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions, ) return relative_pos def forward( self, temporal_factor, hidden_states, attention_mask, output_hidden_states=True, output_attentions=False, query_states=None, relative_pos=None, return_dict=True, train_mode=True ): if attention_mask.dim() <= 2: input_mask = attention_mask else: input_mask = (attention_mask.sum(-2) > 0).byte() attention_mask = self.get_attention_mask(attention_mask) relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos) all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None if isinstance(hidden_states, Sequence): next_kv = hidden_states[0] else: next_kv = hidden_states rel_embeddings = self.get_rel_embedding() output_states = next_kv loss_moe = 0 loads = [] embeddings = [] for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (output_states,) output_states, _, load = layer_module( temporal_factor, next_kv, attention_mask, output_attentions, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, train_mode=train_mode ) if isinstance(load, torch.Tensor): loads.append(load) if _: loss_moe = loss_moe + _ if output_attentions: output_states, att_m = output_states if i == 0 and self.conv is not None: output_states = self.conv(hidden_states, output_states, input_mask) 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 output_attentions: all_attentions = all_attentions + (att_m,) if output_hidden_states: all_hidden_states = all_hidden_states + (output_states,) if not return_dict: return tuple( v for v in [output_states, all_hidden_states, all_attentions] if v is not None ) if len(loads)>0: loads = torch.stack(loads, dim = 0) if len(embeddings) >0: embeddings = torch.cat(embeddings, dim=0) return BaseModelOutput( last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions, loss_moe=loss_moe, loads=loads ) def make_log_bucket_position(relative_pos, bucket_size, max_position): sign = np.sign(relative_pos) mid = bucket_size // 2 abs_pos = np.where( (relative_pos < mid) & (relative_pos > -mid), mid - 1, np.abs(relative_pos) ) log_pos = ( np.ceil(np.log(abs_pos / mid) / np.log((max_position - 1) / mid) * (mid - 1)) + mid ) bucket_pos = np.where(abs_pos <= mid, relative_pos, log_pos * sign).astype(np.int64) return bucket_pos def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1): """ Build relative position according to the query and key We assume the absolute position of query :math:`P_q` is range from (0, query_size) and the absolute position of key :math:`P_k` is range from (0, key_size), The relative positions from query to key is :math:`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 Return: :obj:`torch.LongTensor`: A tensor with shape [1, query_size, key_size] """ q_ids = np.arange(0, query_size) k_ids = np.arange(0, key_size) rel_pos_ids = q_ids[:, None] - np.tile(k_ids, (q_ids.shape[0], 1)) 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 = torch.tensor(rel_pos_ids, dtype=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)) ) class DisentangledSelfAttention(nn.Module): """ Disentangled self-attention module Parameters: config (:obj:`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 :class:`~transformers.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 = StableDropout(config.hidden_dropout_prob) if not self.share_att_key: if "c2p" in self.pos_att_type or "p2p" 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 or "p2p" in self.pos_att_type: self.pos_query_proj = nn.Linear( config.hidden_size, self.all_head_size ) self.dropout = StableDropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x, attention_heads): 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, return_att=False, query_states=None, relative_pos=None, rel_embeddings=None, ): """ Call the module Args: hidden_states (:obj:`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 (:obj:`torch.ByteTensor`): 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. return_att (:obj:`bool`, optional): Whether return the attention matrix. query_states (:obj:`torch.FloatTensor`, optional): The `Q` state in `Attention(Q,K,V)`. relative_pos (:obj:`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 (:obj:`torch.FloatTensor`): The embedding of relative distances. It's a tensor of shape [:math:`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 if "p2p" in self.pos_att_type: scale_factor += 1 scale = math.sqrt(query_layer.size(-1) * scale_factor) attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale 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), ) # bsz x height x length x dimension attention_probs = XSoftmax.apply(attention_scores, attention_mask, -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 return_att: return (context_layer, attention_probs) else: return context_layer def disentangled_attention_bias( self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor ): if relative_pos is None: q = query_layer.size(-2) relative_pos = build_relative_position( q, key_layer.size(-2), 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.long().to(query_layer.device) rel_embeddings = rel_embeddings[ self.pos_ebd_size - att_span : self.pos_ebd_size + att_span, : ].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 or "p2p" 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 or "p2p" 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 = math.sqrt(pos_key_layer.size(-1) * 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 # position->content if "p2c" in self.pos_att_type or "p2p" in self.pos_att_type: scale = math.sqrt(pos_query_layer.size(-1) * scale_factor) if key_layer.size(-2) != query_layer.size(-2): r_pos = build_relative_position( key_layer.size(-2), key_layer.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions, ).to(query_layer.device) r_pos = r_pos.unsqueeze(0) else: r_pos = relative_pos p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1) if query_layer.size(-2) != key_layer.size(-2): pos_index = relative_pos[:, :, :, 0].unsqueeze(-1) if "p2c" in self.pos_att_type: 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) if query_layer.size(-2) != key_layer.size(-2): p2c_att = torch.gather( p2c_att, dim=-2, index=pos_index.expand( p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)) ), ) score += p2c_att / scale # position->position if "p2p" in self.pos_att_type: pos_query = pos_query_layer[:, :, att_span:, :] p2p_att = torch.matmul(pos_query, pos_key_layer.transpose(-1, -2)) p2p_att = p2p_att.expand(query_layer.size()[:2] + p2p_att.size()[2:]) if query_layer.size(-2) != key_layer.size(-2): p2p_att = torch.gather( p2p_att, dim=-2, index=pos_index.expand( query_layer.size()[:2] + (pos_index.size(-2), p2p_att.size(-1)) ), ) p2p_att = torch.gather( p2p_att, dim=-1, index=c2p_pos.expand( [ query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1), ] ), ) score += p2p_att return score # Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm class DebertaV2Embeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__( self, config, features_dim, add_video_feat=False, max_feats = 10 ): 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) self.position_embeddings = nn.Embedding( config.max_position_embeddings, self.embedding_size ) # it is used for the decoder anyway if config.type_vocab_size > 0: self.token_type_embeddings = nn.Embedding( config.type_vocab_size, self.embedding_size ) if self.embedding_size != config.hidden_size: self.embed_proj = nn.Linear( self.embedding_size, config.hidden_size, bias=False ) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = StableDropout(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)) ) self.add_video_feat = add_video_feat self.features_dim = features_dim if self.features_dim: self.linear_video = nn.Linear(features_dim, config.hidden_size) if self.add_video_feat: self.evl = EVLTransformer(max_feats, decoder_num_layers=1, decoder_qkv_dim=768, add_video_feat=self.add_video_feat, add_mask=True) #self.evl = ConvNet() def get_video_embedding(self, video, video_mask): if self.add_video_feat: video_g = self.evl(video, video_mask) video_feat = self.linear_video(video) video_feat_l = torch.cat([video_g, video_feat], dim = 1) else: video_feat_l = self.linear_video(video) video_feat_tmp = video_feat_l * video_mask.unsqueeze(-1) video_g = torch.sum(video_feat_tmp, dim = 1) / video_mask.sum(dim = 1, keepdim=True) return video_g, video_feat_l def forward( self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None, video=None, video_mask=None ): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) if self.features_dim and video is not None: video_global, video = self.get_video_embedding(video, video_mask) inputs_embeds = torch.cat([video, inputs_embeds], 1) input_shape = inputs_embeds[:, :, 0].shape 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 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.config.type_vocab_size > 0: token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = embeddings + token_type_embeddings if self.embedding_size != self.config.hidden_size: 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": embeddings, "position_embeddings": position_embeddings, "video_global": video_global } # Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2 class DebertaV2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DebertaV2Config base_model_prefix = "deberta" _keys_to_ignore_on_load_missing = ["position_ids"] _keys_to_ignore_on_load_unexpected = ["position_embeddings"] def __init__(self, config): super().__init__(config) self._register_load_state_dict_pre_hook(self._pre_load_hook) 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_() def _pre_load_hook( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, ): """ Removes the classifier if it doesn't have the correct number of labels. """ self_state = self.state_dict() if ( ("classifier.weight" in self_state) and ("classifier.weight" in state_dict) and self_state["classifier.weight"].size() != state_dict["classifier.weight"].size() ): print( f"The checkpoint classifier head has a shape {state_dict['classifier.weight'].size()} and this model " f"classifier head has a shape {self_state['classifier.weight'].size()}. Ignoring the checkpoint " f"weights. You should train your model on new data." ) del state_dict["classifier.weight"] if "classifier.bias" in state_dict: del state_dict["classifier.bias"] # Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2 class DebertaV2Model(DebertaV2PreTrainedModel): def __init__( self, config, max_feats=10, features_dim=768, freeze_lm=False, ds_factor_attn=8, ds_factor_ff=8, ft_ln=False, dropout=0.1, add_video_feat = False, freeze_ad=False, ): super().__init__(config) self.embeddings = DebertaV2Embeddings( config, features_dim, add_video_feat, max_feats ) self.encoder = DebertaV2Encoder( config, ds_factor_attn, ds_factor_ff, dropout, ) self.z_steps = 0 self.config = config self.features_dim = features_dim self.max_feats = max_feats if freeze_lm: for n, p in self.named_parameters(): #if (not "linear_video" in n) and (not "adapter" in n): # if ft_ln and "LayerNorm" in n: # continue # else: # p.requires_grad_(False) if not freeze_ad: if (not "evl" in n) and (not "linear_video" in n) and (not "adapter" in n) and (not "moe" in n): if ft_ln and "LayerNorm" in n: continue else: p.requires_grad_(False) else: if not "evl" in n: p.requires_grad_(False) self.init_weights() 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." ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, video=None, video_mask=None, train_mode = True ): 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: 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 self.features_dim and video is not None: if video_mask is None: video_shape = video[:, :, 0].size() video_mask = torch.ones(video_shape, device=device) attention_mask = torch.cat([video_mask, attention_mask], 1) input_shape = attention_mask.size() 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, video=video, video_mask=video_mask[:, 1:] if video_mask.shape[1] != video.shape[1] else video_mask ) embedding_output, position_embeddings, video_g = ( embedding_output["embeddings"], embedding_output["position_embeddings"], embedding_output["video_global"] ) video_g = video_g.squeeze() encoder_outputs = self.encoder( video_g, embedding_output, attention_mask, output_hidden_states=True, output_attentions=output_attentions, return_dict=return_dict, train_mode=train_mode ) encoded_layers = encoder_outputs[1] loss_moe =encoder_outputs.loss_moe 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, return_att=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, position_embeddings=position_embeddings, attention_mask=attention_mask, video_g=video_g, loss_moe = loss_moe, loads=encoder_outputs.loads ) # Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM with Deberta->DebertaV2 class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel): _keys_to_ignore_on_load_unexpected = [r"pooler"] _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] def __init__( self, config, max_feats=10, features_dim=768, freeze_lm=True, freeze_mlm=True, ds_factor_attn=8, ds_factor_ff=8, ft_ln=True, dropout=0.1, n_ans=0, freeze_last=True, add_video_feat = False, freeze_ad=False, add_temporal_trans = False ): """ :param config: BiLM configuration :param max_feats: maximum number of frames used by the model :param features_dim: embedding dimension of the visual features, set = 0 for text-only mode :param freeze_lm: whether to freeze or not the language model (Transformer encoder + token embedder) :param freeze_mlm: whether to freeze or not the MLM head :param ds_factor_attn: downsampling factor for the adapter after self-attention, no adapter if set to 0 :param ds_factor_ff: downsampling factor for the adapter after feed-forward, no adapter if set to 0 :param ft_ln: whether to finetune or not the normalization layers :param dropout: dropout probability in the adapter :param n_ans: number of answers in the downstream vocabulary, set = 0 during cross-modal training :param freeze_last: whether to freeze or not the answer embedding module """ super().__init__(config) # self.clip, _ = clip.load("ViT-L/14") # for p in self.clip.parameters(): # p.requires_grad_(False) self.deberta = DebertaV2Model( config, max_feats, features_dim, freeze_lm, ds_factor_attn, ds_factor_ff, ft_ln, dropout, add_video_feat, freeze_ad ) self.add_video_feat = add_video_feat self.lm_predictions = DebertaV2OnlyMLMHead(config) self.features_dim = features_dim if freeze_mlm: for n, p in self.lm_predictions.named_parameters(): if ft_ln and "LayerNorm" in n: continue else: p.requires_grad_(False) self.init_weights() self.n_ans = n_ans if n_ans: self.answer_embeddings = nn.Embedding( n_ans, self.deberta.embeddings.embedding_size ) self.answer_bias = nn.Parameter(torch.zeros(n_ans)) if freeze_last: self.answer_embeddings.requires_grad_(False) self.answer_bias.requires_grad_(False) def set_answer_embeddings(self, a2tok, freeze_last=True): a2v = self.deberta.embeddings.word_embeddings(a2tok) # answer embeddings (ans_vocab_num, 1, dim) pad_token_id = getattr(self.config, "pad_token_id", 0) sum_tokens = (a2tok != pad_token_id).sum(1, keepdims=True) # n_ans (1000, 1) n_tokens if len(a2v) != self.n_ans: # reinitialize the answer embeddings assert not self.training self.n_ans = len(a2v) self.answer_embeddings = nn.Embedding( self.n_ans, self.deberta.embeddings.embedding_size ).to(self.device) self.answer_bias.requires_grad = False self.answer_bias.resize_(self.n_ans) self.answer_embeddings.weight.data = torch.div( (a2v * (a2tok != pad_token_id).float()[:, :, None]).sum(1), sum_tokens.clamp(min=1), ) # n_ans a2b = self.lm_predictions.lm_head.bias[a2tok] self.answer_bias.weight = torch.div( (a2b * (a2tok != pad_token_id).float()).sum(1), sum_tokens.clamp(min=1) ) if freeze_last: self.answer_embeddings.requires_grad_(False) self.answer_bias.requires_grad_(False) def emd_context_layer(self, encoder_layers, z_states, attention_mask, encoder, temporal_factor, train_mode): if attention_mask.dim() <= 2: extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) att_mask = extended_attention_mask.byte() attention_mask = att_mask * att_mask.squeeze(-2).unsqueeze(-1) elif attention_mask.dim() == 3: attention_mask = attention_mask.unsqueeze(1) hidden_states = encoder_layers[-2] if not self.config.position_biased_input: layers = [encoder.layer[-1] for _ in range(2)] z_states = z_states + hidden_states query_states = z_states query_mask = attention_mask outputs = [] rel_embeddings = encoder.get_rel_embedding() for layer in layers: output = layer( temporal_factor, hidden_states, query_mask, return_att=False, query_states=query_states, relative_pos=None, rel_embeddings=rel_embeddings, train_mode=train_mode ) query_states = output[0] outputs.append(query_states) else: outputs = [encoder_layers[-1]] return outputs def forward( self, input_ids=None, attention_mask=None, labels=None, video=None, video_mask=None, train_mode=False, ): token_type_ids=None position_ids=None inputs_embeds=None output_attentions=None return_dict=None mlm=False r""" labels (:obj:`torch.LongTensor` of shape :obj:`(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 ) # rand_video = torch.randn(1,30,3,224,224).cuda() # video = self.clip.encode_image(rand_video.squeeze()).unsqueeze(0) # video = video.to(torch.float) 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=True, return_dict=return_dict, video=video, video_mask=video_mask, train_mode = train_mode ) loss_moe = outputs['loss_moe'] if labels is not None: if ( self.features_dim and video is not None ): # ignore the label predictions for visual tokens video_shape = video[:, :, 0].size() # add video_general if self.add_video_feat: video_shape = (video_shape[0], video_shape[1] + 1) video_labels = torch.tensor( [[-100] * video_shape[1]] * video_shape[0], dtype=torch.long, device=labels.device, ) labels = torch.cat([video_labels, labels], 1) # sequence_output = outputs[0] modified = self.emd_context_layer( encoder_layers=outputs["hidden_states"], z_states=outputs["position_embeddings"].repeat( input_ids.shape[0] // len(outputs["position_embeddings"]), 1, 1 ), attention_mask=outputs["attention_mask"], encoder=self.deberta.encoder, temporal_factor=outputs["video_g"], train_mode = train_mode ) bias = None if self.n_ans and (not mlm): # downstream mode embeddings = self.answer_embeddings.weight bias = self.answer_bias else: embeddings = self.deberta.embeddings.word_embeddings.weight prediction_scores = self.lm_predictions(modified[-1], embeddings, bias) 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), # labels[labels > 0].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_moe=loss_moe, loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, loads=outputs.loads, embeddings=outputs.video_g ) # copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta class DebertaV2PredictionHeadTransform(nn.Module): 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) 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 # copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta class DebertaV2LMPredictionHead(nn.Module): 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) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. 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` def forward(self, hidden_states, embedding_weight, bias=None): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) if bias is not None: logits = ( torch.matmul(hidden_states, embedding_weight.t().to(hidden_states)) + bias ) else: logits = ( torch.matmul(hidden_states, embedding_weight.t().to(hidden_states)) + self.bias ) return logits # copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta class DebertaV2OnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() # self.predictions = DebertaV2LMPredictionHead(config) self.lm_head = DebertaV2LMPredictionHead(config) def forward(self, sequence_output, embedding_weight, bias=None): prediction_scores = self.lm_head(sequence_output, embedding_weight, bias=bias) return prediction_scores