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class MegatronBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = MegatronBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decod... | 9,412 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MegatronBertLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return predict... | 9,413 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertOnlyNSPHead(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 | 9,414 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MegatronBertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = s... | 9,415 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MegatronBertConfig
load_tf_weights = load_tf_weights_in_megatron_bert
base_model_prefix = "bert... | 9,416 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertForPreTrainingOutput(ModelOutput):
"""
Output type of [`MegatronBertForPreTraining`]. | 9,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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... | 9,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
shape `(batch_size, sequence_length, hidden_size)`. | 9,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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... | 9,417 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertModel(MegatronBertPreTrainedModel):
"""
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://... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
self.embeddings = MegatronBertEmbeddings(config)
self.encoder = MegatronBertEncoder(config)
self.pooler = MegatronBertPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
@add_start_docstrings_to_model_forward(MEGATRON_BERT_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(
sel... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, 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 encod... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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 ... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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)`.
use_cache (`bool`... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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()... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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,
... | 9,418 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertForPreTraining(MegatronBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config, add_binary_head=True):
super().__init__(config)
self.bert = MegatronBertModel(config)
self.cls = MegatronBertPreTrainingHeads(config)
# In... | 9,419 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
@add_start_docstrings_to_model_forward(MEGATRON_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MegatronBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_m... | 9,419 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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]`
next_... | 9,419 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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
>>> fr... | 9,419 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_st... | 9,419 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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 MegatronBertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
... | 9,419 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertForCausalLM(MegatronBertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `MegatronBertForCausalLM` as a standalone,... | 9,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
@add_start_docstrings_to_model_forward(MEGATRON_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.LongTensor] = None,
attention_... | 9,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
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... | 9,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ... | 9,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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)`.
use_cache (`bool`... | 9,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
>>> prediction_logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
tok... | 9,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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()
... | 9,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past | 9,420 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertForMaskedLM(MegatronBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `MegatronBertForMaskedLM` make sure `config.is... | 9,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
@add_start_docstrings_to_model_forward(MEGATRON_BERT_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... | 9,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
r"""
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
... | 9,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embe... | 9,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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,
... | 9,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
# add a dummy token
if self.config.pad_token_id is None:
raise ValueError("The PAD token should be defined for generation")
attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
dummy_token = torch.full(
(effective_b... | 9,421 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertForNextSentencePrediction(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = MegatronBertModel(config)
self.cls = MegatronBertOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init() | 9,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
@add_start_docstrings_to_model_forward(MEGATRON_BERT_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.LongTensor] = None,
attention_mask: ... | 9,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`: | 9,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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, MegatronBertForNextSentencePrediction
>>> import torch
>>> tokenizer = AutoToke... | 9,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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")
... | 9,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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... | 9,422 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertForSequenceClassification(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = MegatronBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn... | 9,423 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
@add_start_docstrings_to_model_forward(MEGATRON_BERT_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... | 9,423 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
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 (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 9,423 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_st... | 9,423 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_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... | 9,423 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertForMultipleChoice(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = MegatronBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize... | 9,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
@add_start_docstrings_to_model_forward(
MEGATRON_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(
... | 9,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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... | 9,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
... | 9,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits... | 9,424 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertForTokenClassification(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = MegatronBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | 9,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
@add_start_docstrings_to_model_forward(MEGATRON_BERT_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: O... | 9,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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 | 9,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_st... | 9,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,425 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertForQuestionAnswering(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = MegatronBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_lab... | 9,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
@add_start_docstrings_to_model_forward(MEGATRON_BERT_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... | 9,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
... | 9,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_st... | 9,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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:
... | 9,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.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,
... | 9,426 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py |
class MegatronBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MegatronBertModel`]. It is used to instantiate a
MEGATRON_BERT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults w... | 9,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/configuration_megatron_bert.py |
Args:
vocab_size (`int`, *optional*, defaults to 29056):
Vocabulary size of the MEGATRON_BERT model. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`MegatronBertModel`].
hidden_size (`int`, *optional*, defaults to 1024):
... | 9,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/configuration_megatron_bert.py |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings,... | 9,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/configuration_megatron_bert.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Typ... | 9,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/configuration_megatron_bert.py |
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`. | 9,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/configuration_megatron_bert.py |
Examples:
```python
>>> from transformers import MegatronBertConfig, MegatronBertModel
>>> # Initializing a MEGATRON_BERT google-bert/bert-base-uncased style configuration
>>> configuration = MegatronBertConfig()
>>> # Initializing a model (with random weights) from the google-bert/bert-base-unca... | 9,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/configuration_megatron_bert.py |
def __init__(
self,
vocab_size=29056,
hidden_size=1024,
num_hidden_layers=24,
num_attention_heads=16,
intermediate_size=4096,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
... | 9,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/configuration_megatron_bert.py |
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
... | 9,427 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/configuration_megatron_bert.py |
class GPTBigCodeAttention(nn.Module):
def __init__(self, config, is_cross_attention=False, layer_idx=None):
super().__init__()
self.config = config
self.mask_value = None
self.multi_query = config.multi_query
self.embed_dim = config.hidden_size
self.num_heads = confi... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
self.layer_idx = layer_idx
self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
self.scale_attention_softmax_in_fp32 = (
config.scale_attention_softmax_in_fp32 and config.attention_softmax_in_fp32
)
self.attn_pdrop = config.attn_pdrop
if self.is_cross_at... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
def _get_mask_value(self, device, dtype):
# torch.where expects a tensor. We use a cache to avoid recreating it every time.
if self.mask_value is None or self.mask_value.dtype != dtype or self.mask_value.device != device:
self.mask_value = torch.full([], torch.finfo(dtype).min, dtype=dtype, ... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# MQA models: (batch_size, query_length, num_heads * head_dim)
# MHA models: (batch_size, num_heads, query_length, head_dim)
query_shape = query.shape
batch_size = query_shape[0]
key_length = key.size(-1)
if self.multi_query:
# (batch_size, query_length, num_heads, he... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
query_length = query_shape[2]
attn_shape = (batch_size, self.num_heads, query_length, key_length)
attn_view = (batch_size * self.num_heads, query_length, key_length)
# Always copies
query = query.reshape(batch_size * self.num_heads, query_length, self.head_dim)
... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
attn_weights = torch.empty(attn_view, device=query.device, dtype=query.dtype)
if query.device.type == "cpu":
# This is needed because of a bug in pytorch https://github.com/pytorch/pytorch/issues/80588.
# The bug was fixed in https://github.com/pytorch/pytorch/pull/96086,
# b... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if upcast:
# Use a fused kernel to prevent a large overhead from casting and scaling.
# Sub-optimal when the key length is not a multiple of 8.
if attention_mask is None:
attn_weights = upcast_softmax(attn_weights, unscale, softmax_dtype)
else:
... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# Mask heads if we want to
if head_mask is not None:
if self.multi_query:
head_mask = head_mask.transpose(1, 2)
attn_weights = attn_weights * head_mask
if self.multi_query:
attn_output = torch.bmm(attn_weights.view(attn_view), value).view(query_shape)... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Option... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
query = self.q_attn(hidden_states)
key_value = self.c_attn(encoder_hidden_states)
attention_mask = encoder_attention_mask
elif self.multi_query:
query, key_value = self.c_attn(hidden_states).split((self.embed_dim, 2 * self.kv_dim), dim=2)
else:
# Note: We ... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
key, value = key_value.split((self.head_dim, self.head_dim), dim=-1)
attn_output, attn_weights = self._attn(query, key.transpose(-1, -2), value, attention_mask, head_mask)
if not self.multi_query:
attn_output = attn_output.transpose(1, 2).reshape(hidden_states.shape)
attn_output = ... | 9,428 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodeFlashAttention2(GPTBigCodeAttention):
"""
GPTBigCode flash attention module. This module inherits from `GPTBigCodeAttention` as the weights of the module
stays untouched. The only required change would be on the forward pass where it needs to correctly call the public
API of flash attent... | 9,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-... | 9,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Option... | 9,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
query = self.q_attn(hidden_states)
key_value = self.c_attn(encoder_hidden_states)
attention_mask = encoder_attention_mask
elif self.multi_query:
query, key_value = self.c_attn(hidden_states).split((self.embed_dim, 2 * self.kv_dim), dim=2)
else:
# Note: We ... | 9,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
key, value = key_value.split((self.head_dim, self.head_dim), dim=-1)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
if self.multi_query:
batch_size, query_length, _ = query.shape
query = query.reshape(batch_size, ... | 9,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in float16 just to be sure everything works as expected.
input_dtype = query.dtype
if input_dtype == t... | 9,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query = ... | 9,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
if output_attentions:
if self.multi_query:
# Transpose to return weights in the usual format (batch_size, num_heads, query_length, key_length)
attn_weights_reshaped = attn_weights_reshaped.transpose(1, 2)
else:
attn_weights_reshaped = None
outputs... | 9,429 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
class GPTBigCodeSdpaAttention(GPTBigCodeAttention):
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
if head_mask is not None:
# The super dispatch is done in the forward.
raise ValueError(
"PyTorch SDPA does not support head_mask. Please open ... | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# Without these unsqueeze, SDPA complains as the query and key/value have a different number of dimensions.
key = key.unsqueeze(1)
value = value.unsqueeze(1)
# Although these expand are not numerically useful, PyTorch can not dispatch to memory-efficient backend
# and fl... | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
# See the comment above.
if query.device.type == "cuda" and attention_mask is not None:
query = query.contiguous()
key = key.contiguous()
value = value.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if s... | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
sdpa_result = torch.nn.functional.scaled_dot_product_attention(
query,
key,
value,
attn_mask=attention_mask,
dropout_p=self.attn_pdrop if self.training else 0.0,
is_causal=is_causal,
scale=scale,
)
if self.multi_query:
... | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Option... | 9,430 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py |
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