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|
| """ PyTorch DeBERTa-v2 model. """
|
| import pdb
|
| import math
|
| from collections.abc import Sequence
|
|
|
| import numpy as np
|
| import torch
|
| from torch import _softmax_backward_data, nn
|
| from torch.nn import CrossEntropyLoss, LayerNorm
|
|
|
| from transformers.activations import ACT2FN
|
| from transformers.file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
|
| from transformers.modeling_outputs import (
|
| BaseModelOutput,
|
| MaskedLMOutput,
|
| QuestionAnsweringModelOutput,
|
| SequenceClassifierOutput,
|
| TokenClassifierOutput,
|
| )
|
| from transformers.modeling_utils import PreTrainedModel
|
| from transformers.utils import logging
|
| from transformers.models.deberta_v2.configuration_deberta_v2 import DebertaV2Config
|
|
|
|
|
| logger = logging.get_logger(__name__)
|
|
|
| _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 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):
|
|
|
|
|
|
|
| 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
|
|
|
|
|
|
|
| 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
|
|
|
|
|
|
|
| class DropoutContext(object):
|
| def __init__(self):
|
| self.dropout = 0
|
| self.mask = None
|
| self.scale = 1
|
| self.reuse_mask = True
|
|
|
|
|
|
|
| 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
|
|
|
|
|
|
|
| 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
|
|
|
|
|
|
|
| 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
|
|
|
|
|
|
|
| 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 = StableDropout(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
|
|
|
|
|
|
|
| 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,
|
| return_att=False,
|
| query_states=None,
|
| relative_pos=None,
|
| rel_embeddings=None,
|
| ):
|
| 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 = self.output(self_output, query_states)
|
|
|
| if return_att:
|
| return (attention_output, att_matrix)
|
| else:
|
| return attention_output
|
|
|
|
|
|
|
| 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
|
|
|
|
|
|
|
| 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 = StableDropout(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
|
|
|
|
|
|
|
| class DebertaV2Layer(nn.Module):
|
| 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,
|
| return_att=False,
|
| query_states=None,
|
| relative_pos=None,
|
| rel_embeddings=None,
|
| ):
|
| attention_output = self.attention(
|
| hidden_states,
|
| attention_mask,
|
| return_att=return_att,
|
| query_states=query_states,
|
| relative_pos=relative_pos,
|
| rel_embeddings=rel_embeddings,
|
| )
|
| if return_att:
|
| attention_output, att_matrix = attention_output
|
| intermediate_output = self.intermediate(attention_output)
|
| layer_output = self.output(intermediate_output, attention_output)
|
| if return_att:
|
| return (layer_output, att_matrix)
|
| else:
|
| return layer_output
|
|
|
|
|
| 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):
|
| 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
|
|
|
| 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,
|
| 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).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
|
| for i, layer_module in enumerate(self.layer):
|
|
|
| if output_hidden_states:
|
| all_hidden_states = all_hidden_states + (output_states,)
|
|
|
| output_states = layer_module(
|
| next_kv,
|
| attention_mask,
|
| output_attentions,
|
| query_states=query_states,
|
| relative_pos=relative_pos,
|
| rel_embeddings=rel_embeddings,
|
| )
|
| 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)
|
| return BaseModelOutput(
|
| last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions
|
| )
|
|
|
|
|
| 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(int)
|
| 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
|
|
|
| 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
|
|
|
| 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
|
|
|
| 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
|
|
|
| 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)
|
| )
|
|
|
|
|
| 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)
|
|
|
| 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
|
| )
|
| 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
|
| )
|
|
|
| score = 0
|
|
|
| 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
|
|
|
|
|
| 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
|
|
|
|
|
| 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
|
|
|
|
|
|
|
| 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)
|
|
|
| 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
|
|
|
|
|
| self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
|
|
|
| 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 += position_embeddings
|
| if self.config.type_vocab_size > 0:
|
| token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
| 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
|
|
|
|
|
|
|
|
|
| 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):
|
|
|
|
|
| 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()
|
| ):
|
| logger.warning(
|
| 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"]
|
|
|
|
|
|
|
| DEBERTA_START_DOCSTRING = r"""
|
| The DeBERTa model was proposed in `DeBERTa: Decoding-enhanced BERT with Disentangled Attention
|
| <https://arxiv.org/abs/2006.03654>`_ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build on top of
|
| BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
|
| improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.
|
|
|
| This model is also a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__
|
| subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to
|
| general usage and behavior.```
|
|
|
|
|
| Parameters:
|
| config (:class:`~transformers.DebertaV2Config`): Model configuration class with all the parameters of the model.
|
| Initializing with a config file does not load the weights associated with the model, only the
|
| configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
|
| weights.
|
| """
|
|
|
| DEBERTA_INPUTS_DOCSTRING = r"""
|
| Args:
|
| input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`):
|
| Indices of input sequence tokens in the vocabulary.
|
|
|
| Indices can be obtained using :class:`transformers.DebertaV2Tokenizer`. See
|
| :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.__call__` for
|
| details.
|
|
|
| `What are input IDs? <../glossary.html#input-ids>`__
|
| attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`):
|
| Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
|
|
|
| - 1 for tokens that are **not masked**,
|
| - 0 for tokens that are **masked**.
|
|
|
| `What are attention masks? <../glossary.html#attention-mask>`__
|
| token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`):
|
| Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
|
| 1]``:
|
|
|
| - 0 corresponds to a `sentence A` token,
|
| - 1 corresponds to a `sentence B` token.
|
|
|
| `What are token type IDs? <../glossary.html#token-type-ids>`_
|
| position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`):
|
| Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
|
| config.max_position_embeddings - 1]``.
|
|
|
| `What are position IDs? <../glossary.html#position-ids>`_
|
| inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
|
| Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
|
| This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
| than the model's internal embedding lookup matrix.
|
| output_attentions (:obj:`bool`, `optional`):
|
| Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
|
| tensors for more detail.
|
| output_hidden_states (:obj:`bool`, `optional`):
|
| Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
|
| more detail.
|
| return_dict (:obj:`bool`, `optional`):
|
| Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
|
| """
|
|
|
|
|
|
|
| @add_start_docstrings(
|
| "The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.",
|
| DEBERTA_START_DOCSTRING,
|
| )
|
|
|
| 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
|
| 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.")
|
|
|
|
|
| @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
| @add_code_sample_docstrings(
|
| checkpoint=_CHECKPOINT_FOR_DOC,
|
| output_type=SequenceClassifierOutput,
|
| config_class=_CONFIG_FOR_DOC,
|
| )
|
| 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,
|
| ):
|
| 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 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,
|
| 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,
|
| )
|
|
|
|
|
|
|
|
|
| @add_start_docstrings("""DeBERTa Model with a `language modeling` head on top. """, DEBERTA_START_DOCSTRING)
|
|
|
| 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):
|
| super().__init__(config)
|
|
|
| self.deberta = DebertaV2Model(config)
|
| self.cls = DebertaV2OnlyMLMHead(config)
|
|
|
| self.init_weights()
|
|
|
| def get_output_embeddings(self):
|
| return self.cls.predictions.decoder
|
|
|
| def set_output_embeddings(self, new_embeddings):
|
| self.cls.predictions.decoder = new_embeddings
|
|
|
|
|
| @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
| @add_code_sample_docstrings(
|
| checkpoint=_CHECKPOINT_FOR_DOC,
|
| output_type=MaskedLMOutput,
|
| config_class=_CONFIG_FOR_DOC,
|
| )
|
| def forward(
|
| self,
|
| input_ids=None,
|
| attention_mask=None,
|
| token_type_ids=None,
|
| position_ids=None,
|
| inputs_embeds=None,
|
| labels=None,
|
| output_attentions=None,
|
| output_hidden_states=None,
|
| return_dict=None,
|
| ):
|
| 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
|
|
|
| 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]
|
| prediction_scores = self.cls(sequence_output)
|
|
|
| masked_lm_loss = None
|
| if labels is not None:
|
| loss_fct = CrossEntropyLoss()
|
| masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
|
|
| if not return_dict:
|
| output = (prediction_scores,) + outputs[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,
|
| )
|
|
|
|
|
|
|
|
|
| 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
|
|
|
|
|
|
|
| class DebertaV2LMPredictionHead(nn.Module):
|
| def __init__(self, config):
|
| super().__init__()
|
| self.transform = DebertaV2PredictionHeadTransform(config)
|
|
|
|
|
|
|
| self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
| self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
|
|
|
|
| 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 DebertaV2OnlyMLMHead(nn.Module):
|
| def __init__(self, config):
|
| super().__init__()
|
| self.predictions = DebertaV2LMPredictionHead(config)
|
|
|
| def forward(self, sequence_output):
|
| prediction_scores = self.predictions(sequence_output)
|
| return prediction_scores
|
|
|
|
|
|
|
| @add_start_docstrings(
|
| """
|
| 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.
|
| """,
|
| DEBERTA_START_DOCSTRING,
|
| )
|
|
|
| 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 = StableDropout(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)
|
|
|
|
|
| @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
| @add_code_sample_docstrings(
|
| checkpoint=_CHECKPOINT_FOR_DOC,
|
| output_type=SequenceClassifierOutput,
|
| config_class=_CONFIG_FOR_DOC,
|
| )
|
| def forward(
|
| self,
|
| input_ids=None,
|
| attention_mask=None,
|
| token_type_ids=None,
|
| position_ids=None,
|
| inputs_embeds=None,
|
| labels=None,
|
| output_attentions=None,
|
| output_hidden_states=None,
|
| return_dict=None,
|
| ):
|
| r"""
|
| labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
|
| Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
|
| config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
|
| If :obj:`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.num_labels == 1:
|
|
|
| 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()
|
| if not return_dict:
|
| output = (logits,) + outputs[1:]
|
| return ((loss,) + output) if loss is not None else output
|
| else:
|
| return SequenceClassifierOutput(
|
| loss=loss,
|
| logits=logits,
|
| hidden_states=outputs.hidden_states,
|
| attentions=outputs.attentions,
|
| )
|
|
|
|
|
|
|
|
|
| @add_start_docstrings(
|
| """
|
| DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
|
| Named-Entity-Recognition (NER) tasks.
|
| """,
|
| DEBERTA_START_DOCSTRING,
|
| )
|
|
|
| class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
|
| _keys_to_ignore_on_load_unexpected = [r"pooler"]
|
|
|
| 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)
|
|
|
| self.init_weights()
|
|
|
|
|
| @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
| @add_code_sample_docstrings(
|
| checkpoint=_CHECKPOINT_FOR_DOC,
|
| output_type=TokenClassifierOutput,
|
| config_class=_CONFIG_FOR_DOC,
|
| )
|
| def forward(
|
| self,
|
| input_ids=None,
|
| attention_mask=None,
|
| token_type_ids=None,
|
| position_ids=None,
|
| inputs_embeds=None,
|
| labels=None,
|
| output_attentions=None,
|
| output_hidden_states=None,
|
| return_dict=None,
|
| ):
|
| r"""
|
| labels (:obj:`torch.LongTensor` of shape :obj:`(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_weights = torch.zeros(len(self.config.label2id)).to(self.device)
|
| loss_weights[self.config.label2id["SOLUTION-CORRECT"]] = self.config.task_specific_params["solution_correct_loss_weight"]
|
| loss_weights[self.config.label2id["SOLUTION-INCORRECT"]] = self.config.task_specific_params["solution_incorrect_loss_weight"]
|
| loss_weights[self.config.label2id["STEP-CORRECT"]] = self.config.task_specific_params["step_correct_loss_weight"]
|
| loss_weights[self.config.label2id["STEP-INCORRECT"]] = self.config.task_specific_params["step_incorrect_loss_weight"]
|
| loss_weights[self.config.label2id["O"]] = self.config.task_specific_params["other_label_loss_weight"]
|
|
|
|
|
| loss_fct = CrossEntropyLoss(weight=loss_weights)
|
|
|
|
|
| if attention_mask is not None:
|
| active_loss = attention_mask.view(-1) == 1
|
| active_logits = logits.view(-1, self.num_labels)
|
| active_labels = torch.where(
|
| active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
|
| )
|
| loss = loss_fct(active_logits, active_labels)
|
| else:
|
| 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,
|
| )
|
|
|
|
|
|
|
|
|
| @add_start_docstrings(
|
| """
|
| DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
|
| layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
| """,
|
| DEBERTA_START_DOCSTRING,
|
| )
|
|
|
| class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
|
| _keys_to_ignore_on_load_unexpected = [r"pooler"]
|
|
|
| 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)
|
|
|
| self.init_weights()
|
|
|
|
|
| @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
| @add_code_sample_docstrings(
|
| checkpoint=_CHECKPOINT_FOR_DOC,
|
| output_type=QuestionAnsweringModelOutput,
|
| config_class=_CONFIG_FOR_DOC,
|
| )
|
| def forward(
|
| self,
|
| input_ids=None,
|
| attention_mask=None,
|
| token_type_ids=None,
|
| position_ids=None,
|
| inputs_embeds=None,
|
| start_positions=None,
|
| end_positions=None,
|
| output_attentions=None,
|
| output_hidden_states=None,
|
| return_dict=None,
|
| ):
|
| r"""
|
| start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
|
| Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
| Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
|
| sequence are not taken into account for computing the loss.
|
| end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
|
| Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
| Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
|
| sequence are not taken into account for computing the loss.
|
| """
|
| 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 len(start_positions.size()) > 1:
|
| start_positions = start_positions.squeeze(-1)
|
| if len(end_positions.size()) > 1:
|
| end_positions = end_positions.squeeze(-1)
|
|
|
| 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,
|
| )
|
|
|
