norbert3-base / modeling_norbert.py
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import math
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils import checkpoint
from .configuration_norbert import NorbertConfig
from transformers.modeling_utils import PreTrainedModel
from transformers.activations import gelu_new
from transformers.modeling_outputs import (
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
BaseModelOutput
)
from transformers.pytorch_utils import softmax_backward_data
class Encoder(nn.Module):
def __init__(self, config, activation_checkpointing=False):
super().__init__()
self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.num_hidden_layers)])
for i, layer in enumerate(self.layers):
layer.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
layer.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i)))
self.activation_checkpointing = activation_checkpointing
def forward(self, hidden_states, attention_mask, relative_embedding):
hidden_states, attention_probs = [hidden_states], []
for layer in self.layers:
if self.activation_checkpointing:
hidden_state, attention_p = checkpoint.checkpoint(layer, hidden_states[-1], attention_mask, relative_embedding)
else:
hidden_state, attention_p = layer(hidden_states[-1], attention_mask, relative_embedding)
hidden_states.append(hidden_state)
attention_probs.append(attention_p)
return hidden_states, attention_probs
class MaskClassifier(nn.Module):
def __init__(self, config, subword_embedding):
super().__init__()
self.nonlinearity = nn.Sequential(
nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
nn.Linear(config.hidden_size, config.hidden_size),
nn.GELU(),
nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
nn.Dropout(config.hidden_dropout_prob),
nn.Linear(subword_embedding.size(1), subword_embedding.size(0))
)
self.initialize(config.hidden_size, subword_embedding)
def initialize(self, hidden_size, embedding):
std = math.sqrt(2.0 / (5.0 * hidden_size))
nn.init.trunc_normal_(self.nonlinearity[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
self.nonlinearity[-1].weight = embedding
self.nonlinearity[1].bias.data.zero_()
self.nonlinearity[-1].bias.data.zero_()
def forward(self, x, masked_lm_labels=None):
if masked_lm_labels is not None:
x = torch.index_select(x.flatten(0, 1), 0, torch.nonzero(masked_lm_labels.flatten() != -100).squeeze())
x = self.nonlinearity(x)
return x
class EncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = Attention(config)
self.mlp = FeedForward(config)
def forward(self, x, padding_mask, relative_embedding):
attention_output, attention_probs = self.attention(x, padding_mask, relative_embedding)
x = x + attention_output
x = x + self.mlp(x)
return x, attention_probs
class GeGLU(nn.Module):
def forward(self, x):
x, gate = x.chunk(2, dim=-1)
x = x * gelu_new(gate)
return x
class FeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.mlp = nn.Sequential(
nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False),
nn.Linear(config.hidden_size, 2*config.intermediate_size, bias=False),
GeGLU(),
nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps, elementwise_affine=False),
nn.Linear(config.intermediate_size, config.hidden_size, bias=False),
nn.Dropout(config.hidden_dropout_prob)
)
self.initialize(config.hidden_size)
def initialize(self, hidden_size):
std = math.sqrt(2.0 / (5.0 * hidden_size))
nn.init.trunc_normal_(self.mlp[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.mlp[-2].weight, mean=0.0, std=std, a=-2*std, b=2*std)
def forward(self, x):
return self.mlp(x)
class MaskedSoftmax(torch.autograd.Function):
@staticmethod
def forward(self, x, mask, dim):
self.dim = dim
x.masked_fill_(mask, float('-inf'))
x = torch.softmax(x, self.dim)
x.masked_fill_(mask, 0.0)
self.save_for_backward(x)
return x
@staticmethod
def backward(self, grad_output):
output, = self.saved_tensors
input_grad = softmax_backward_data(self, grad_output, output, self.dim, output)
return input_grad, None, None
class Attention(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
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 heads {config.num_attention_heads}")
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_size = config.hidden_size // config.num_attention_heads
self.in_proj_qk = nn.Linear(config.hidden_size, 2*config.hidden_size, bias=True)
self.in_proj_v = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
self.pre_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False)
self.post_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)
position_indices = torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(1) \
- torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(0)
position_indices = self.make_log_bucket_position(position_indices, config.position_bucket_size, config.max_position_embeddings)
position_indices = config.position_bucket_size - 1 + position_indices
self.register_buffer("position_indices", position_indices, persistent=True)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.scale = 1.0 / math.sqrt(3 * self.head_size)
self.initialize()
def make_log_bucket_position(self, relative_pos, bucket_size, max_position):
sign = torch.sign(relative_pos)
mid = bucket_size // 2
abs_pos = torch.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, torch.abs(relative_pos).clamp(max=max_position - 1))
log_pos = torch.ceil(torch.log(abs_pos / mid) / math.log((max_position-1) / mid) * (mid - 1)).int() + mid
bucket_pos = torch.where(abs_pos <= mid, relative_pos, log_pos * sign).long()
return bucket_pos
def initialize(self):
std = math.sqrt(2.0 / (5.0 * self.hidden_size))
nn.init.trunc_normal_(self.in_proj_qk.weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.in_proj_v.weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.out_proj.weight, mean=0.0, std=std, a=-2*std, b=2*std)
self.in_proj_qk.bias.data.zero_()
self.in_proj_v.bias.data.zero_()
self.out_proj.bias.data.zero_()
def compute_attention_scores(self, hidden_states, relative_embedding):
key_len, batch_size, _ = hidden_states.size()
query_len = key_len
if self.position_indices.size(0) < query_len:
position_indices = torch.arange(query_len, dtype=torch.long).unsqueeze(1) \
- torch.arange(query_len, dtype=torch.long).unsqueeze(0)
position_indices = self.make_log_bucket_position(position_indices, self.position_bucket_size, 512)
position_indices = self.position_bucket_size - 1 + position_indices
self.position_indices = position_indices.to(hidden_states.device)
hidden_states = self.pre_layer_norm(hidden_states)
query, key = self.in_proj_qk(hidden_states).chunk(2, dim=2) # shape: [T, B, D]
value = self.in_proj_v(hidden_states) # shape: [T, B, D]
query = query.reshape(query_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
key = key.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
value = value.view(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1)
attention_scores = torch.bmm(query, key.transpose(1, 2) * self.scale)
pos = self.in_proj_qk(self.dropout(relative_embedding)) # shape: [2T-1, 2D]
query_pos, key_pos = pos.view(-1, self.num_heads, 2*self.head_size).chunk(2, dim=2)
query = query.view(batch_size, self.num_heads, query_len, self.head_size)
key = key.view(batch_size, self.num_heads, query_len, self.head_size)
attention_c_p = torch.einsum("bhqd,khd->bhqk", query, key_pos.squeeze(1) * self.scale)
attention_p_c = torch.einsum("bhkd,qhd->bhqk", key * self.scale, query_pos.squeeze(1))
position_indices = self.position_indices[:query_len, :key_len].expand(batch_size, self.num_heads, -1, -1)
attention_c_p = attention_c_p.gather(3, position_indices)
attention_p_c = attention_p_c.gather(2, position_indices)
attention_scores = attention_scores.view(batch_size, self.num_heads, query_len, key_len)
attention_scores.add_(attention_c_p)
attention_scores.add_(attention_p_c)
return attention_scores, value
def compute_output(self, attention_probs, value):
attention_probs = self.dropout(attention_probs)
context = torch.bmm(attention_probs.flatten(0, 1), value) # shape: [B*H, Q, D]
context = context.transpose(0, 1).reshape(context.size(1), -1, self.hidden_size) # shape: [Q, B, H*D]
context = self.out_proj(context)
context = self.post_layer_norm(context)
context = self.dropout(context)
return context
def forward(self, hidden_states, attention_mask, relative_embedding):
attention_scores, value = self.compute_attention_scores(hidden_states, relative_embedding)
attention_probs = MaskedSoftmax.apply(attention_scores, attention_mask, -1)
return self.compute_output(attention_probs, value), attention_probs.detach()
class Embedding(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.word_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.relative_embedding = nn.Parameter(torch.empty(2 * config.position_bucket_size - 1, config.hidden_size))
self.relative_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.initialize()
def initialize(self):
std = math.sqrt(2.0 / (5.0 * self.hidden_size))
nn.init.trunc_normal_(self.relative_embedding, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.word_embedding.weight, mean=0.0, std=std, a=-2*std, b=2*std)
def forward(self, input_ids):
word_embedding = self.dropout(self.word_layer_norm(self.word_embedding(input_ids)))
relative_embeddings = self.relative_layer_norm(self.relative_embedding)
return word_embedding, relative_embeddings
#
# HuggingFace wrappers
#
class NorbertPreTrainedModel(PreTrainedModel):
config_class = NorbertConfig
base_model_prefix = "norbert3"
supports_gradient_checkpointing = True
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, Encoder):
module.activation_checkpointing = value
def _init_weights(self, module):
pass # everything is already initialized
class NorbertModel(NorbertPreTrainedModel):
def __init__(self, config, add_mlm_layer=False):
super().__init__(config)
self.config = config
self.embedding = Embedding(config)
self.transformer = Encoder(config, activation_checkpointing=False)
self.classifier = MaskClassifier(config, self.embedding.word_embedding.weight) if add_mlm_layer else None
def get_input_embeddings(self):
return self.embedding.word_embedding
def set_input_embeddings(self, value):
self.embedding.word_embedding = value
def get_contextualized_embeddings(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None
) -> List[torch.Tensor]:
if input_ids is not None:
input_shape = input_ids.size()
else:
raise ValueError("You have to specify input_ids")
batch_size, seq_length = input_shape
device = input_ids.device
if attention_mask is None:
attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device)
else:
attention_mask = ~attention_mask.bool()
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
static_embeddings, relative_embedding = self.embedding(input_ids.t())
contextualized_embeddings, attention_probs = self.transformer(static_embeddings, attention_mask, relative_embedding)
contextualized_embeddings = [e.transpose(0, 1) for e in contextualized_embeddings]
last_layer = contextualized_embeddings[-1]
contextualized_embeddings = [contextualized_embeddings[0]] + [
contextualized_embeddings[i] - contextualized_embeddings[i - 1]
for i in range(1, len(contextualized_embeddings))
]
return last_layer, contextualized_embeddings, attention_probs
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
if not return_dict:
return (
sequence_output,
*([contextualized_embeddings] if output_hidden_states else []),
*([attention_probs] if output_attentions else [])
)
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=contextualized_embeddings if output_hidden_states else None,
attentions=attention_probs if output_attentions else None
)
class NorbertForMaskedLM(NorbertModel):
_keys_to_ignore_on_load_unexpected = ["head"]
def __init__(self, config):
super().__init__(config, add_mlm_layer=True)
def get_output_embeddings(self):
return self.classifier.nonlinearity[-1].weight
def set_output_embeddings(self, new_embeddings):
self.classifier.nonlinearity[-1].weight = new_embeddings
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
subword_prediction = self.classifier(sequence_output)
subword_prediction[:, :, :106+1] = float("-inf")
masked_lm_loss = None
if labels is not None:
masked_lm_loss = F.cross_entropy(subword_prediction.flatten(0, 1), labels.flatten())
if not return_dict:
output = (
subword_prediction,
*([contextualized_embeddings] if output_hidden_states else []),
*([attention_probs] if output_attentions else [])
)
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=subword_prediction,
hidden_states=contextualized_embeddings if output_hidden_states else None,
attentions=attention_probs if output_attentions else None
)
class Classifier(nn.Module):
def __init__(self, config, num_labels: int):
super().__init__()
drop_out = getattr(config, "cls_dropout", None)
drop_out = config.hidden_dropout_prob if drop_out is None else drop_out
self.nonlinearity = nn.Sequential(
nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
nn.Linear(config.hidden_size, config.hidden_size),
nn.GELU(),
nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False),
nn.Dropout(drop_out),
nn.Linear(config.hidden_size, num_labels)
)
self.initialize(config.hidden_size)
def initialize(self, hidden_size):
std = math.sqrt(2.0 / (5.0 * hidden_size))
nn.init.trunc_normal_(self.nonlinearity[1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
nn.init.trunc_normal_(self.nonlinearity[-1].weight, mean=0.0, std=std, a=-2*std, b=2*std)
self.nonlinearity[1].bias.data.zero_()
self.nonlinearity[-1].bias.data.zero_()
def forward(self, x):
x = self.nonlinearity(x)
return x
class NorbertForSequenceClassification(NorbertModel):
_keys_to_ignore_on_load_unexpected = ["classifier"]
_keys_to_ignore_on_load_missing = ["head"]
def __init__(self, config):
super().__init__(config, add_mlm_layer=False)
self.num_labels = config.num_labels
self.head = Classifier(config, self.num_labels)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
logits = self.head(sequence_output[:, 0, :])
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = nn.MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = nn.BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (
logits,
*([contextualized_embeddings] if output_hidden_states else []),
*([attention_probs] if output_attentions else [])
)
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=contextualized_embeddings if output_hidden_states else None,
attentions=attention_probs if output_attentions else None
)
class NorbertForTokenClassification(NorbertModel):
_keys_to_ignore_on_load_unexpected = ["classifier"]
_keys_to_ignore_on_load_missing = ["head"]
def __init__(self, config):
super().__init__(config, add_mlm_layer=False)
self.num_labels = config.num_labels
self.head = Classifier(config, self.num_labels)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
logits = self.head(sequence_output)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (
logits,
*([contextualized_embeddings] if output_hidden_states else []),
*([attention_probs] if output_attentions else [])
)
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=contextualized_embeddings if output_hidden_states else None,
attentions=attention_probs if output_attentions else None
)
class NorbertForQuestionAnswering(NorbertModel):
_keys_to_ignore_on_load_unexpected = ["classifier"]
_keys_to_ignore_on_load_missing = ["head"]
def __init__(self, config):
super().__init__(config, add_mlm_layer=False)
self.num_labels = config.num_labels
self.head = Classifier(config, self.num_labels)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask)
logits = self.head(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = nn.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,
*([contextualized_embeddings] if output_hidden_states else []),
*([attention_probs] if output_attentions else [])
)
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=contextualized_embeddings if output_hidden_states else None,
attentions=attention_probs if output_attentions else None
)
class NorbertForMultipleChoice(NorbertModel):
_keys_to_ignore_on_load_unexpected = ["classifier"]
_keys_to_ignore_on_load_missing = ["head"]
def __init__(self, config):
super().__init__(config, add_mlm_layer=False)
self.num_labels = getattr(config, "num_labels", 2)
self.head = Classifier(config, self.num_labels)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(flat_input_ids, flat_attention_mask)
logits = self.head(sequence_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (
reshaped_logits,
*([contextualized_embeddings] if output_hidden_states else []),
*([attention_probs] if output_attentions else [])
)
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=contextualized_embeddings if output_hidden_states else None,
attentions=attention_probs if output_attentions else None
)