text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
class RoCBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position, shape, pronunciation and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
... | 9,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
if config.concat_input:
input_dim = config.hidden_size
if self.enable_pronunciation:
pronunciation_dim = config.pronunciation_embed_dim
input_dim += pronunciation_dim
if self.enable_shape:
shape_dim = config.shape_embed_dim
... | 9,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "... | 9,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps user... | 9,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
if self.map_inputs_layer is None:
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_typ... | 9,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
denominator = 1
embedding_in = torch.clone(embeddings)
if self.enable_shape and input_shape_ids is not None:
embedding_shape = self.shape_embed(input_shape_ids)
embedding_in += embedding_shape
denominator += 1
if self.enable_pronunciati... | 9,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
embedding_in = torch.clone(inputs_embeds)
if self.enable_shape:
if input_shape_ids is None:
input_shape_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
embedding_shape = self.shape_embed(input_shape_ids)
embedding_in = torch... | 9,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embedding_in += token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embedding_in += position_embeddings
em... | 9,295 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}... | 9,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":... | 9,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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,
... | 9,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
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... | 9,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
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 ... | 9,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = q... | 9,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("b... | 9,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in RoCBertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention s... | 9,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
i... | 9,296 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertSelfOutput(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)
de... | 9,297 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = ROC_BERT_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = RoCBertSelfOut... | 9,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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) | 9,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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,
... | 9,298 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertIntermediate(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.int... | 9,299 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertOutput(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)
... | 9,300 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertLayer(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 = RoCBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = co... | 9,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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,
... | 9,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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
... | 9,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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,
... | 9,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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... | 9,301 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([RoCBertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False | 9,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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,
... | 9,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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... | 9,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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,
... | 9,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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... | 9,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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,
]
... | 9,302 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertPooler(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 hi... | 9,303 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertPredictionHeadTransform(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.t... | 9,304 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = RoCBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Li... | 9,305 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = RoCBertLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | 9,306 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RoCBertConfig
load_tf_weights = load_tf_weights_in_roc_bert
base_model_prefix = "roc_bert"
suppo... | 9,307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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.... | 9,307 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertModel(RoCBertPreTrainedModel):
"""
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/... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
# Copied from transformers.models.clap.modeling_clap.ClapTextModel.__init__ with ClapText->RoCBert
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = RoCBertEmbeddings(config)
self.encoder = RoCBertEncoder(config)
... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
def set_pronunciation_embeddings(self, value):
self.embeddings.pronunciation_embed = value
def get_shape_embeddings(self):
return self.embeddings.shape_embed
def set_shape_embeddings(self, value):
self.embeddings.shape_embed = value
# Copied from transformers.models.bert.modeling_... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
@add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
expected_output=_EXPECTED_OUTPU... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.Floa... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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 ... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
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 els... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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]
... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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()... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
embedding_output = self.embeddings(
input_ids=input_ids,
input_shape_ids=input_shape_ids,
input_pronunciation_ids=input_pronunciation_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_v... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hid... | 9,308 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertForPreTraining(RoCBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.roc_bert = RoCBertModel(config)
self.cls = RoCBertOnlyMLMHead(config)
# Initia... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
@add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
input_shape_ids: Optional[torch.Tensor... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
labels_input_ids: Optional[torch.Tensor] = None,
labels_input_shape_ids: Optional[torch.Tensor] = None,
labels_input_pronunciation_ids: Optional[torch.Tensor] = None,
labels_attention_mask: Optional[torch.Tensor] = None,
labels_token_type_ids: Optional[torch.Tensor] = None,
outpu... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
attack_input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
attack sample shape ids for computing the contrastive loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
target ids for computing the contrastive loss and masked_lm_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_s... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
target pronunciation ids for computing the contrastive loss and masked_lm_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, ...,... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
kwargs (`Dict[str, any]`, *optional*, defaults to *{}*):
Used to hide legacy arguments that have been deprecated.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, RoCBertForPreTraining
>>> import torch
>>> tokenizer = AutoTokeniz... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
>>> logits = outputs.logits
>>> logits.shape
torch.Size([1, 11, 21128])
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roc_bert(
input_ids,
input_shape_ids=input_shape_ids,
... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
loss = None
if labels_input_ids is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels_input_ids.view(-1))
if attack_input_ids is not None:
batch_size, _ = ... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
labels_output = self.roc_bert(
target_inputs,
input_shape_ids=labels_input_shape_ids,
input_pronunciation_ids=labels_input_pronunciation_ids,
attention_mask=labels_attention_mask,
token_type_ids=labels_token_type_ids,
... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
pooled_output_norm = torch.nn.functional.normalize(pooled_output, dim=-1)
labels_pooled_output_norm = torch.nn.functional.normalize(labels_pooled_output, dim=-1)
attack_pooled_output_norm = torch.nn.functional.normalize(attack_pooled_output, dim=-1)
sim_matrix = torch.ma... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.a... | 9,309 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertForMaskedLM(RoCBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
# Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(conf... | 9,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
# Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
self.cls.predictions.bias = new_embeddings.bias | 9,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
@add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
input_shape_ids: Optional[torch.Tensor] = None,
input_pronunciation_ids: Optional[torch.Tensor] = None,
attenti... | 9,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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... | 9,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
Example:
```python
>>> from transformers import AutoTokenizer, RoCBertForMaskedLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("weiweishi/roc-bert-base-zh")
>>> model = RoCBertForMaskedLM.from_pretrained("weiweishi/roc-bert-base-zh")
>>> inputs = token... | 9,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
outputs = self.roc_bert(
input_ids,
input_shape_ids=input_shape_ids,
input_pronunciation_ids=input_pronunciation_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
... | 9,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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,
... | 9,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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=... | 9,310 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertForCausalLM(RoCBertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
# Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.__init__ with BertLMHeadModel->RoCBertForCausalLM,Bert->RoCBert,bert->roc_bert
def ... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
# Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
self.cls.predictions.bias = new_embeddings.bias | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
@add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
input_shape_ids: Op... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hi... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with eac... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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)`.
labels (`torch.Lo... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
```python
>>> from transformers import AutoTokenizer, RoCBertForCausalLM, RoCBertConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("weiweishi/roc-bert-base-zh")
>>> config = RoCBertConfig.from_pretrained("weiweishi/roc-bert-base-zh")
>>> config.is_decoder = T... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
outputs = self.roc_bert(
input_ids,
input_shape_ids=input_shape_ids,
input_pronunciation_ids=input_pronunciation_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
def prepare_inputs_for_generation(
self,
input_ids,
input_shape_ids=None,
input_pronunciation_ids=None,
past_key_values=None,
attention_mask=None,
**model_kwargs,
):
# Overwritten -- `input_pronunciation_ids`
input_shape = input_ids.shape
... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
input_ids = input_ids[:, remove_prefix_length:]
if input_shape_ids is not None:
input_shape_ids = input_shape_ids[:, -1:]
if input_pronunciation_ids is not None:
input_pronunciation_ids = input_pronunciation_ids[:, -1:]
return {
"input_ids": i... | 9,311 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertForSequenceClassification(RoCBertPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.conf... | 9,312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
@add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_SEQ_CLASS_EXPE... | 9,312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_la... | 9,312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
outputs = self.roc_bert(
input_ids,
input_shape_ids=input_shape_ids,
input_pronunciation_ids=input_pronunciation_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
... | 9,312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
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):
sel... | 9,312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.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... | 9,312 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertForMultipleChoice(RoCBertPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(config)
self.roc_bert = RoCBertModel(config)
classifier_dropout = (
... | 9,313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
@add_start_docstrings_to_model_forward(
ROC_BERT_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(
... | 9,313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the s... | 9,313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
input_shape_ids = input_shape_ids.view(-1, input_shape_ids.size(-1)) if input_shape_ids is not None else None
input_pronunciation_ids = (
input_pronunciation_ids.view(-1, input_pronunciation_ids.size(-1))
... | 9,313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
outputs = self.roc_bert(
input_ids,
input_shape_ids=input_shape_ids,
input_pronunciation_ids=input_pronunciation_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
... | 9,313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,313 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertForTokenClassification(RoCBertPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roc_bert ... | 9,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
@add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_TOKEN_CLASS_EXPECTED... | 9,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
... | 9,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
outputs = self.roc_bert(
input_ids,
input_shape_ids=input_shape_ids,
input_pronunciation_ids=input_pronunciation_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
... | 9,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.