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|---|---|---|
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(sel... | 10,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states ... | 10,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
batch_size, num_attention_heads, from_seq_length, to_seq_length = attention_scores.size()
relations_keys = self.relative_positions_encoding(to_seq_length)
query_layer_t = query_layer.permute(2, 0, 1, 3)
query_layer_r = query_layer_t.contiguous().view(
from_seq_length, batch_size * n... | 10,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in NezhaModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_sc... | 10,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
context_layer = torch.matmul(attention_probs, value_layer)
relations_values = self.relative_positions_encoding(to_seq_length)
attention_probs_t = attention_probs.permute(2, 0, 1, 3)
attentions_probs_r = attention_probs_t.contiguous().view(
from_seq_length, batch_size * num_attention_... | 10,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs | 10,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def ... | 10,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = NezhaSelfAttention(config)
self.output = NezhaSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, inde... | 10,303 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads) | 10,303 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,303 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaIntermediate(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.inter... | 10,304 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
de... | 10,305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = NezhaAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config... | 10,306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
... | 10,306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
... | 10,306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = out... | 10,306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([NezhaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False | 10,307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_ca... | 10,307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
... | 10,307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attent... | 10,307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
... | 10,307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidd... | 10,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaPredictionHeadTransform(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.tra... | 10,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = NezhaPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear... | 10,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = NezhaLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | 10,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score | 10,312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = NezhaLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.prediction... | 10,313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NezhaConfig
load_tf_weights = load_tf_weights_in_nezha
base_model_prefix = "nezha"
supports_gradie... | 10,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
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.... | 10,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaForPreTrainingOutput(ModelOutput):
"""
Output type of [`NezhaForPreTraining`]. | 10,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (`torch.FloatTensor` of shape `(batch_size, seque... | 10,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
shape `(batch_size, sequence_length, hidden_size)`. | 10,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
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... | 10,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaModel(NezhaPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
self.embeddings = NezhaEmbeddings(config)
self.encoder = NezhaEncoder(config)
self.pooler = NezhaPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embed... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-atte... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed ... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
embedding_output = self.embeddings(
input_ids=input_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
... | 10,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaForPreTraining(NezhaPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
self.nezha = NezhaModel(config)
self.cls = NezhaPreTrainingHeads(config)
# Initialize weights and apply final processing
... | 10,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=NezhaForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch... | 10,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
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]`
... | 10,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
kwargs (`Dict[str, any]`, optional, defaults to *{}*):
Used to hide legacy arguments that have been deprecated.
Returns:
Example:
```python
... | 10,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 10,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return NezhaForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_re... | 10,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaForMaskedLM(NezhaPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `NezhaForMaskedLM` make sure `config.is_decoder=False` for "... | 10,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
@add_start_docstrings_to_model_forward(NEZHA_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: Optional[torch.T... | 10,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
l... | 10,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_... | 10,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
... | 10,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
dummy_token = torch.full(
(effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
)
input_ids = torch.cat([input_ids, dummy_token], dim=... | 10,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaForNextSentencePrediction(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.nezha = NezhaModel(config)
self.cls = NezhaOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init() | 10,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[tor... | 10,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
(see `input_ids` docstring). Indices should be in `[0, 1]`: | 10,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, NezhaForNextSentencePrediction
>>> import torch
>>> tokenizer = AutoTokenizer.f... | 10,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if "next_sentence_label" in kwargs:
warnings.warn(
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
" `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("next_sentence_label")
... | 10,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
next_sentence_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence... | 10,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaForSequenceClassification(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.nezha = NezhaModel(config)
classifier_dropout = (
config.classifier_dropout if config.cl... | 10,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
@add_start_docstrings_to_model_forward(NEZHA_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: Option... | 10,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 10,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 10,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaForMultipleChoice(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.nezha = NezhaModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
se... | 10,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
@add_start_docstrings_to_model_forward(NEZHA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
inp... | 10,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.co... | 10,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 10,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 10,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaForTokenClassification(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.nezha = NezhaModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifie... | 10,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
@add_start_docstrings_to_model_forward(NEZHA_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: Optional[... | 10,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
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 | 10,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 10,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class NezhaForQuestionAnswering(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.nezha = NezhaModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Init... | 10,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
@add_start_docstrings_to_model_forward(NEZHA_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: Op... | 10,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
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 (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.Lo... | 10,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
outputs = self.nezha(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 10,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
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:
... | 10,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
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,
... | 10,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py |
class MMBTConfig:
"""
This is the configuration class to store the configuration of a [`MMBTModel`]. It is used to instantiate a MMBT
model according to the specified arguments, defining the model architecture.
Args:
config ([`PreTrainedConfig`]):
Config of the underlying Transforme... | 10,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/configuration_mmbt.py |
class ModalEmbeddings(nn.Module):
"""Generic Modal Embeddings which takes in an encoder, and a transformer embedding."""
def __init__(self, config, encoder, embeddings):
super().__init__()
self.config = config
self.encoder = encoder
self.proj_embeddings = nn.Linear(config.modal_... | 10,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
if start_token is not None:
start_token_embeds = self.word_embeddings(start_token)
seq_length += 1
token_embeddings = torch.cat([start_token_embeds.unsqueeze(1), token_embeddings], dim=1)
if end_token is not None:
end_token_embeds = self.word_embeddings(end_token... | 10,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = token_embeddings + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
... | 10,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
class MMBTModel(nn.Module, ModuleUtilsMixin):
def __init__(self, config, transformer, encoder):
super().__init__()
self.config = config
self.transformer = transformer
self.modal_encoder = ModalEmbeddings(config, encoder, transformer.embeddings)
@add_start_docstrings_to_model_for... | 10,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
```python
# For example purposes. Not runnable.
transformer = BertModel.from_pretrained("google-bert/bert-base-uncased")
encoder = ImageEncoder(args)
mmbt = MMBTModel(config, transformer, encoder)
```"""
output_attentions = output_attentions if output_attentions is not No... | 10,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
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_txt_shape = input_ids.size()
elif inputs_embeds is not None:
input_txt_shape = inputs_emb... | 10,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
txt_embeddings = self.transformer.embeddings(
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
)
embedding_output = torch.cat([modal_embeddings, txt_embeddings], 1)
input_shape = embedding_output.size()[:-1]
if atte... | 10,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
encoder_outputs = self.transformer.encoder(
... | 10,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def get_input_embeddings(self):
return self.embeddings.word_embedd... | 10,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
class MMBTForClassification(nn.Module):
r"""
**labels**: (*optional*) `torch.LongTensor` of shape `(batch_size,)`:
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (... | 10,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
Returns: *Tuple* comprising various elements depending on the configuration (config) and inputs: **loss**:
(*optional*, returned when `labels` is provided) `torch.FloatTensor` of shape `(1,)`: Classification (or
regression if config.num_labels==1) loss. **logits**:
`torch.FloatTensor` of shape `(batch_s... | 10,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
to compute the weighted average in the self-attention heads. | 10,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
Examples:
```python
# For example purposes. Not runnable.
transformer = BertModel.from_pretrained("google-bert/bert-base-uncased")
encoder = ImageEncoder(args)
model = MMBTForClassification(config, transformer, encoder)
outputs = model(input_modal, input_ids, labels=labels)
loss, logits = o... | 10,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
def forward(
self,
input_modal,
input_ids=None,
modal_start_tokens=None,
modal_end_tokens=None,
attention_mask=None,
token_type_ids=None,
modal_token_type_ids=None,
position_ids=None,
modal_position_ids=None,
head_mask=None,
... | 10,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
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