text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
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,
... | 10,500 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.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... | 10,500 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.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,
]
... | 10,500 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmPooler(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... | 10,501 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmEmbedderOutput(ModelOutput):
"""
Outputs of [`RealmEmbedder`] models.
Args:
projected_score (`torch.FloatTensor` of shape `(batch_size, config.retriever_proj_size)`):
Projected score.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden... | 10,502 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
projected_score: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None | 10,502 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmScorerOutput(ModelOutput):
"""
Outputs of [`RealmScorer`] models.
Args:
relevance_score (`torch.FloatTensor` of shape `(batch_size, config.num_candidates)`):
The relevance score of document candidates (before softmax).
query_score (`torch.FloatTensor` of shape `(batch... | 10,503 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmReaderOutput(ModelOutput):
"""
Outputs of [`RealmReader`] models. | 10,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `start_positions`, `end_positions`, `has_answers` are provided):
Total loss.
retriever_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `start_positions`, `end_positions`, `has_answers` are provide... | 10,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
candidate (`torch.LongTensor` of shape `()`):
The index of the retrieved span candidates in which the predicted answer is most likely.
start_pos (`torch.IntTensor` of shape `()`):
Predicted answer starting position in *RealmReader*'s inputs.
end_pos (`torch.IntTensor` of shape `(... | 10,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.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... | 10,504 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmForOpenQAOutput(ModelOutput):
"""
Outputs of [`RealmForOpenQA`] models.
Args:
reader_output (`dict`):
Reader output.
predicted_answer_ids (`torch.LongTensor` of shape `(answer_sequence_length)`):
Predicted answer ids.
"""
reader_output: dict = No... | 10,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmPredictionHeadTransform(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... | 10,506 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = RealmPredictionHeadTransform(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... | 10,507 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = RealmLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores | 10,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmScorerProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = RealmLMPredictionHead(config)
self.dense = nn.Linear(config.hidden_size, config.retriever_proj_size)
self.LayerNorm = nn.LayerNorm(config.retriever_proj_size, eps=config.layer_no... | 10,509 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmReaderProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense_intermediate = nn.Linear(config.hidden_size, config.span_hidden_size * 2)
self.dense_output = nn.Linear(config.span_hidden_size, 1)
self.layer_normalization ... | 10,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def _spans_given_width(width):
current_starts = torch.arange(max_sequence_len - width + 1, device=masks.device)
current_ends = torch.arange(width - 1, max_sequence_len, device=masks.device)
return current_starts, current_ends
starts, ends = zip(*(_spans_given... | 10,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# [reader_beam_size, max_sequence_len, span_hidden_size * 2]
hidden_states = self.dense_intermediate(hidden_states)
# [reader_beam_size, max_sequence_len, span_hidden_size]
start_projection, end_projection = hidden_states.chunk(2, dim=-1)
candidate_starts, candidate_ends, candidate_mask... | 10,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# [reader_beam_size, num_candidates, span_hidden_size]
candidate_hidden = self.relu(candidate_hidden)
# [reader_beam_size, num_candidates, span_hidden_size]
candidate_hidden = self.layer_normalization(candidate_hidden)
# [reader_beam_size, num_candidates]
reader_logits = self.den... | 10,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RealmConfig
load_tf_weights = load_tf_weights_in_realm
base_model_prefix = "realm" | 10,511 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.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.... | 10,511 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def _flatten_inputs(self, *inputs):
"""Flatten inputs' shape to (-1, input_shape[-1])"""
flattened_inputs = []
for tensor in inputs:
if tensor is None:
flattened_inputs.append(None)
else:
input_shape = tensor.shape
if len(in... | 10,511 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmBertModel(RealmPreTrainedModel):
"""
Same as the original BertModel but remove docstrings.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = RealmEmbeddings(config)
self.encoder = RealmEnco... | 10,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_he... | 10,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
... | 10,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.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:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 10,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffer... | 10,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.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()... | 10,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.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... | 10,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embeddi... | 10,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.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,
... | 10,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmEmbedder(RealmPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.realm = RealmBertModel(self.config)
self.cls = RealmScorerProjection(self.config)
self.post_init()
def get_input_embed... | 10,513 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
@add_start_docstrings_to_model_forward(REALM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=RealmEmbedderOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.F... | 10,513 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
>>> tokenizer = AutoTokenizer.from_pretrained("google/realm-cc-news-pretrained-embedder")
>>> model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> project... | 10,513 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# [batch_size, hidden_size]
pooler_output = realm_outputs[1]
# [batch_size, retriever_proj_size]
projected_score = self.cls(pooler_output)
if not return_dict:
return (projected_score,) + realm_outputs[2:4]
else:
return RealmEmbedderOutput(
... | 10,513 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmScorer(RealmPreTrainedModel):
r"""
Args:
query_embedder ([`RealmEmbedder`]):
Embedder for input sequences. If not specified, it will use the same embedder as candidate sequences.
"""
def __init__(self, config, query_embedder=None):
super().__init__(config)
... | 10,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
@add_start_docstrings_to_model_forward(REALM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=RealmScorerOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Flo... | 10,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
) -> Union[Tuple, RealmScorerOutput]:
r"""
candidate_input_ids (`torch.LongTensor` of shape `(batch_size, num_candidates, sequence_length)`):
Indices of candidate input sequence tokens in the vocabulary. | 10,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
candidate_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_candidates, sequence_length)`, *opt... | 10,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
[What are token type IDs?](../glossary#token-type-ids)
candidate_inputs_embeds (`torch.FloatTensor` of shape `(batch_size * num_candidates, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `candidate_input_ids` you can choose to directly pass an embedded
repres... | 10,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
>>> # batch_size = 2, num_candidates = 2
>>> input_texts = ["How are you?", "What is the item in the picture?"]
>>> candidates_texts = [["Hello world!", "Nice to meet you!"], ["A cute cat.", "An adorable dog."]]
>>> inputs = tokenizer(input_texts, return_tensors="pt")
>>> candidates_inp... | 10,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
if candidate_input_ids is None and candidate_inputs_embeds is None:
raise ValueError("You have to specify either candidate_input_ids or candidate_inputs_embeds.")
query_outputs = self.query_embedder(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_... | 10,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
candidate_outputs = self.embedder(
flattened_input_ids,
attention_mask=flattened_attention_mask,
token_type_ids=flattened_token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=candidate_inputs_embeds,
output_atte... | 10,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
if not return_dict:
return relevance_score, query_score, candidate_score
return RealmScorerOutput(
relevance_score=relevance_score, query_score=query_score, candidate_score=candidate_score
) | 10,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmKnowledgeAugEncoder(RealmPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
self.realm = RealmBertModel(self.config)
self.cls = RealmOnlyMLMHead(self.config)
self.post_init()
def get_input_embed... | 10,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
@add_start_docstrings_to_model_forward(
REALM_INPUTS_DOCSTRING.format("batch_size, num_candidates, sequence_length")
)
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attenti... | 10,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
relevance_score (`torch.FloatTensor` of shape `(batch_size, num_candidates)`, *optional*):
Relevance score derived from RealmScorer, must be specified if you want to compute the masked language
modeling loss. | 10,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.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... | 10,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
>>> tokenizer = AutoTokenizer.from_pretrained("google/realm-cc-news-pretrained-encoder")
>>> model = RealmKnowledgeAugEncoder.from_pretrained(
... "google/realm-cc-news-pretrained-encoder", num_candidates=2
... )
>>> # batch_size = 2, num_candidates = 2
>>> text = [["Hello w... | 10,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
(flattened_input_ids, flattened_attention_mask, flattened_token_type_ids) = self._flatten_inputs(
input_ids, attention_mask, token_type_ids
)
joint_outputs = self.realm(
flattened_input_ids,
attention_mask=flattened_attention_mask,
token_type_ids=flattene... | 10,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
masked_lm_loss = None
if labels is not None:
batch_size, seq_length = labels.size()
if mlm_mask is None:
mlm_mask = torch.ones_like(labels, dtype=torch.float32)
else:
mlm_mask = mlm_mask.type(torch.float32)
# Compute marginal log-... | 10,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# [batch_size * num_candidates * joint_seq_len, vocab_size]
mlm_logits = prediction_scores.view(-1, self.config.vocab_size)
# [batch_size * num_candidates * joint_seq_len]
mlm_targets = labels.tile(1, self.config.num_candidates).view(-1)
# [batch_size, num_candidates, joi... | 10,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
if not return_dict:
output = (prediction_scores,) + joint_outputs[2:4]
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=joint_outputs.hidd... | 10,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmReader(RealmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.realm = RealmBertModel(config)
self.cls = RealmOnlyMLMHead(config)
self.qa_outputs = RealmReaderProjection(config)
self.post_init() | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
@add_start_docstrings_to_model_forward(REALM_INPUTS_DOCSTRING.format("reader_beam_size, sequence_length"))
@replace_return_docstrings(output_type=RealmReaderOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[tor... | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
) -> Union[Tuple, RealmReaderOutput]:
r"""
relevance_score (`torch.FloatTensor` of shape `(searcher_beam_size,)`, *optional*):
Relevance score, which must be specified if you want to compute the logits and marginal log loss.
block_mask (`torch.BoolTensor` of shape `(searcher_beam_siz... | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
has_answers (`torch.BoolTe... | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if relevance_score is None:
raise ValueError("You have to specify `relevance_score` to calculate logits and loss.")
if block_mask is None:
raise ValueError("You hav... | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# [reader_beam_size, joint_seq_len, hidden_size]
sequence_output = outputs[0]
# [reader_beam_size, num_candidates], [num_candidates], [num_candidates]
reader_logits, candidate_starts, candidate_ends = self.qa_outputs(
sequence_output, block_mask[0 : self.config.reader_beam_size]
... | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
total_loss = None
retriever_loss = None
reader_loss = None
retriever_correct = None
reader_correct = None
if start_positions is not None and end_positions is not None and has_answers is not None:
def compute_correct_candidates(candidate_starts, candidate_ends, gold_s... | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def marginal_log_loss(logits, is_correct):
"""Loss based on the negative marginal log-likelihood."""
def mask_to_score(mask, dtype=torch.float32):
return (1.0 - mask.type(dtype)) * torch.finfo(dtype).min
# []
log_numerator = torch.log... | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
reader_correct = compute_correct_candidates(
candidate_starts=candidate_starts,
candidate_ends=candidate_ends,
gold_starts=start_positions[0 : self.config.reader_beam_size],
gold_ends=end_positions[0 : self.config.reader_beam_size],
)
... | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
if not return_dict:
output = (predicted_block_index, predicted_candidate, predicted_start, predicted_end) + outputs[2:]
return (
((total_loss, retriever_loss, reader_loss, retriever_correct, reader_correct) + output)
if total_loss is not None
else ... | 10,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmForOpenQA(RealmPreTrainedModel):
def __init__(self, config, retriever=None):
super().__init__(config)
self.embedder = RealmEmbedder(config)
self.reader = RealmReader(config)
self.register_buffer(
"block_emb",
torch.zeros(()).new_empty(
... | 10,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
@add_start_docstrings_to_model_forward(REALM_FOR_OPEN_QA_DOCSTRING.format("1, sequence_length"))
@replace_return_docstrings(output_type=RealmForOpenQAOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor],
attention_mask: Optional[torch.FloatTensor... | 10,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
>>> question = "Who is the pioneer in modern computer science?"
>>> question_ids = tokenizer([question], return_tensors="pt")
>>> answer_ids = tokenizer(
... ["alan mathison turing"],
... add_special_tokens=False,
... return_token_type_ids=False,
... retur... | 10,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
question_outputs = self.embedder(
input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, return_dict=True
)
# [1, projection_size]
question_projection = question_outputs[0]
# CPU computation starts.
# [1, block_emb_size]
batch_scor... | 10,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# Retrieve possible answers
has_answers, start_pos, end_pos, concat_inputs = self.retriever(
retrieved_block_ids.cpu(), input_ids, answer_ids, max_length=self.config.reader_seq_len
)
concat_inputs = concat_inputs.to(self.reader.device)
block_mask = concat_inputs.special_toke... | 10,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
reader_output = self.reader(
input_ids=concat_inputs.input_ids[0 : self.config.reader_beam_size],
attention_mask=concat_inputs.attention_mask[0 : self.config.reader_beam_size],
token_type_ids=concat_inputs.token_type_ids[0 : self.config.reader_beam_size],
relevance_score=... | 10,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class GPTSanJapaneseDenseActDense(nn.Module):
"""
FFN Layer for Switch Transformer and Extra layers
GPTSAN can mix Switch Transformer layers and normal Transformer layers This class is used as Expert in Switch
Transformer layers and as FFN in regular Transformer layers. RELU is used in the Switch Trans... | 10,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def forward(self, hidden_states):
r"""
Args:
hidden_states (`torch.Tensor`) :
[num_groups, tokens_per_group, hidden_dim] inputs to send to experts.
Returns:
torch.Tensor[num_groups, tokens_per_group, hidden_dim]
"""
hidden_states = self.wi... | 10,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseTop1Router(nn.Module):
"""
Router using tokens choose top-1 experts assignment.
This router uses the same mechanism as in Switch Transformer (https://arxiv.org/abs/2101.03961) and V-MoE
(https://arxiv.org/abs/2106.05974): tokens choose their top experts. Items are sorted by router_p... | 10,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def _compute_router_probabilities(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
r"""
Computes router probabilities from input hidden states. | 10,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
Args:
hidden_states (`torch.Tensor`):
(batch_size, sequence_length, hidden_dim) from which router probabilities are computed.
Returns:
router_probabilities (`torch.Tensor`):
Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the pr... | 10,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if self.training and self.jitter_noise > 0:
# Multiply the token inputs by the uniform distribution - adding some noise
hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
# Shape: [num_groups, tokens_per_group, num_experts]
... | 10,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def _cast_classifier(self):
r"""
`bitsandbytes` `Linear8bitLt` layers does not support manual casting Therefore we need to check if they are an
instance of the `Linear8bitLt` class by checking special attributes.
"""
if not (hasattr(self.classifier, "SCB") or hasattr(self.classif... | 10,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
Each Router works as the following: it expects the hidden states for each token, gets the `router_probs` and
`router_logits` from the `router_weights`. This will assign for each token, the raw probability to be assigned
to an expert. Then each Router class will have to define its own `_compute_routing_i... | 10,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
# Mask tokens outside expert capacity. Sum over each sequence
token_priority = torch.cumsum(expert_index, dim=-2)
# mask if the token routed to to the expert will overflow
expert_capacity_mask = token_priority <= self.expert_capacity
expert_index = expert_index * expert_capacity_mask
... | 10,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseSparseMLP(nn.Module):
r"""
Implementation of the Switch Transformers Sparse MLP module.
"""
def __init__(self, config: GPTSanJapaneseConfig, expert_class: nn.Module = GPTSanJapaneseDenseActDense):
super().__init__()
# Step 1: Get the correct router according to its c... | 10,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
1- Gets the `router_mask` from the router. The shape of the mask is `(batch_size, sequence_length, num_expert)`
and corresponds to the argmax of the `router_probs`. The probabilities are needed in the computation of the
hidden states : they are broadcasted to the hidden states values (can be interpreted... | 10,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
next_states = hidden_states.clone()
for idx, expert in enumerate(self.experts.values()):
token_indices = router_mask[:, :, idx].bool()
next_states[token_indices] = expert(hidden_states[token_indices]).to(next_states.dtype)
hidden_states = router_probs * next_states
retur... | 10,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseLayerSparseFF(nn.Module):
r"""
Switch Transformers Feed Forward layer module. This is a wrapper around the Mixture of Experts module.
Parameters:
config : ([`GPTSanJapaneseConfig`]): Model configuration class with all the parameters of the model.
Initializing with a ... | 10,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def forward(self, hidden_states, output_router_logits):
r"""
Args:
hidden_states (`torch.Tensor`) :
[num_groups, tokens_per_group, hidden_dim] inputs to send to experts.
output_router_logits (`bool`) :
output experts router output.
Returns:... | 10,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseLayerDenseFF(nn.Module):
r"""
Extra Transformers Feed Forward layer module.
Parameters:
config : ([`GPTSanJapaneseConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the ... | 10,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
def forward(self, hidden_states):
r"""
Args:
hidden_states (`torch.Tensor`) :
[num_groups, tokens_per_group, hidden_dim] inputs to send to experts.
Returns:
torch.Tensor[num_groups, tokens_per_group, hidden_dim]
"""
forwarded_states = self... | 10,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int... | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size() | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support... | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_sta... | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
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 (first "if" case)
# if uni-directional self-attention (d... | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
... | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = la... | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_we... | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
... | 10,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
class GPTSanJapaneseLayerSelfAttention(nn.Module):
"""
Self Attention and Normalization Unit
"""
def __init__(self, config, has_relative_attention_bias=False):
super().__init__()
self.self_attn = GPTSanJapaneseAttention(
embed_dim=config.d_model,
num_heads=config... | 10,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
Args:
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-attention
if the model is configured as a decoder.
past_key_va... | 10,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, ... | 10,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
head_mask (`numpy.ndarray` of shape `({0})`, `optional):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not ma... | 10,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py |
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