text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Args:
hidden_states (`torch.Tensor` of shape `(target_sequence_length, batch_size, hidden_size)`):
Hidden states to be updated by the Mega block
attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indicates which entrie... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Encoder hidden states to be used for cross-attention (and required for encoder-decoder model setup)
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, source_sequence_length)`, *optional*):
Indicates which entries in the cross/source sequence are to be ignored (mostly due to p... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Whether to perfom incremental decoding; uses `past_key_value` as prior state, and returns the updated
states for use in the next step | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Returns:
`tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and
inputs:
- **hidden_states** (`torch.FloatTensor` of shape `(target_sequence_length, batch_size, hidden_size)`) --
Hidden states from target sequence updated b... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
- **self_key** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
sequence_length, config.shared_representation_size)` -- The self-attention key state for use in the next
step of incremental decoding
- **self_value** (*optional*, returned when... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
The cross-attention key state for use in the next step of incremental decoding
- **cross_value** (*optional*, returned when `use_cache=True` and `config.is_decoder=True`)
`torch.FloatTensor` of shape `(batch_size, source_sequence_length, config.hidden_size)` -- The
cross-attentio... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# incremental decoding in the MegaMultiDimensionDampedEma module requires that the attention mask has the same
# sequence length as the input tensor; if we're caching incremental states, we assume the input
# sequence length is 1 (Mega will break otherwise), so we take the padding mask for the final
... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
new_hidden_states = mega_outputs[0]
self_key, self_value, self_ema_state = mega_outputs[-3:] if use_cache else (None, None, None)
self_attention_weights = mega_outputs[1] if output_attentions else None
# optional cross attention
if self.cross_attn is not None:
if encoder_hid... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# update the hidden state from cross attention
new_hidden_states = cross_attn_outputs[0]
# store cross-attention k/v if caching
cross_key, cross_value = cross_attn_outputs[-2:] if use_cache else (None, None)
cross_attention_weights = cross_attn_outputs[1] if output_attent... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
outs = outs + (new_key_values,)
return outs | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaPooler(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 hidde... | 10,344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MegaConfig
base_model_prefix = "mega"
supports_gradient_checkpointing = False
_no_split_modules = [... | 10,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, MegaMultiDimensionDampedEma):
with torch.no_grad():
# delta & alpha
nn.init.normal_(module.damping_factor, mean=0.0, std=self.config.ema_delta_alpha_range)
nn.i... | 10,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
nn.init.normal_(module.residual_weight, mean=0.0, std=self.config.ema_gamma_omega_range)
elif isinstance(module, MegaSimpleRelativePositionalBias):
nn.init.normal_(module.rel_pos_bias, mean=0.0, std=self.config.initializer_range)
elif isinstance(module, MegaRotaryRelativePositionalBias):
... | 10,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
nn.init.normal_(module.qk_weight, mean=0.0, std=self.config.initializer_range)
nn.init.constant_(module.qk_bias, 0.0)
elif isinstance(module, nn.Linear):
# initializes all linear layers in the entire network
module.weight.data.normal_(mean=0.0, std=self.config.initializer_ran... | 10,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaModel(MegaPreTrainedModel):
"""
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 after self-attention, following the architecture described in *Mega: Moving Average
Equipped Gated Attention*_ by Xuezhe Ma, Ch... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def __init__(self, config: MegaConfig, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embedding_layer = MegaEmbeddings(config)
self.layers = nn.ModuleList([MegaBlock(config) for _ in range(config.num_hidden_layers)])
self.pooler = MegaPooler(config)... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
@add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.Fl... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed ... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.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... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
if self.config.use_chunking:
input_shape = torch.tensor([input_shape[0], self.config.chunk_size])
batch_size, sequence_length = input_shape
if self.config.use_chunking and (sequence_length > self.config.chunk_size):
if sequence_length % self.config.chunk_size != 0:
... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# Mega expects the causal mask to be a 2D square matrix of (from) x (to) over the input sequence length
# the HF utility function generates a 3D causal mask which includes batch size, so we'll create a dummy
# mask with the correct device and all ones
temp_mask_for_extension = torch.... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# if using cache, make sure we have a tuple of tuples which matches the length of our hidden layers
if (past_key_values is not None) and (len(past_key_values) != self.config.num_hidden_layers):
raise ValueError(
f"Received past key/value cache with size mismatch; expected {self.confi... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# we expect encoder hidden states to also have batch first in line
# with typical Hugging Face behavior (which is also how we return them)
# Mega expects sequence length first, so do the same transpose here
if encoder_hidden_states is not None:
encoder_hidden_states = encoder_hidden_... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# pass through mega layers
all_hidden_states = (embedding_output,) if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache e... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
hidden_states = mega_outputs[0]
if output_hidden_states:
# store layer-wise hidden states in the way that the user expects
# (seq len X batch X embed dim) --> (batch X seq len X embed dim)
all_hidden_states += (hidden_states.transpose(0, 1),)
if ou... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
if not return_dict:
return (hidden_states, pooled_output) + (
all_hidden_states,
next_decoder_cache,
all_self_attentions,
all_cross_attentions,
)
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_... | 10,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaForCausalLM(MegaPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: MegaConfig):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `MegaForCausalLM` as a standalone, add `is_decoder=True.`")
sel... | 10,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
@add_start_docstrings_to_model_forward(MEGA_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_mask: Opt... | 10,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.Fl... | 10,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.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,... | 10,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`... | 10,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
>>> tokenizer = AutoTokenizer.from_pretrained("mnaylor/mega-base-wikitext")
>>> config = AutoConfig.from_pretrained("mnaylor/mega-base-wikitext")
>>> config.is_decoder = True
>>> config.bidirectional = False
>>> model = MegaForCausalLM.from_pretrained(
... "mnaylor/mega-base-... | 10,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
outputs = self.mega(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_... | 10,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.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()
... | 10,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention... | 10,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaForMaskedLM(MegaPreTrainedModel):
_tied_weights_keys = ["mlm_head.weight"]
def __init__(self, config: MegaConfig):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `MegaForMaskedLM`, set `config.is_decoder=False` for "
... | 10,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def set_output_embeddings(self, new_embeddings):
self.mlm_head = new_embeddings | 10,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
@add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<mask>",
expected_output="' Paris'",
expect... | 10,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.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... | 10,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
outputs = self.mega(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=out... | 10,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
... | 10,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaForSequenceClassification(MegaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.mega = MegaModel(config, add_pooling_layer=False)
self.classifier = MegaClassificationHead(config)
... | 10,349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
@add_start_docstrings_to_model_forward(MEGA_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: Optiona... | 10,349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
outputs = self.mega(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
... | 10,349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 10,349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaForMultipleChoice(MegaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mega = MegaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final... | 10,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
@add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
inpu... | 10,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
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.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds... | 10,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None e... | 10,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
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, labels)
if not return_dic... | 10,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaForTokenClassification(MegaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mega = MegaModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dr... | 10,351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
@add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[t... | 10,351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
outputs = self.mega(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
... | 10,351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier_dr... | 10,352 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaForQuestionAnswering(MegaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mega = MegaModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initiali... | 10,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
@add_start_docstrings_to_model_forward(MEGA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Opt... | 10,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.Lo... | 10,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
outputs = self.mega(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
... | 10,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 10,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 10,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class JukeboxPriorConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`JukeboxPrior`]. It is used to instantiate a
`JukeboxPrior` according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults ... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
Args:
act_fn (`str`, *optional*, defaults to `"quick_gelu"`):
Activation function.
alignment_head (`int`, *optional*, defaults to 2):
Head that is responsible of the alignment between lyrics and music. Only used to compute the lyric to audio
alignment
alignmen... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
Dropout probability for the post-attention layer dropout in the decoder.
attn_res_scale (`bool`, *optional*, defaults to `False`):
Whether or not to scale the residuals in the attention conditioner block.
blocks (`int`, *optional*, defaults to 64):
Number of blocks used in the `b... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
Configuration of the encoder which models the prior on the lyrics.
encoder_loss_fraction (`float`, *optional*, defaults to 0.4):
Multiplication factor used in front of the lyric encoder loss.
hidden_size (`int`, *optional*, defaults to 2048):
Hidden dimension of the attention lay... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
max_nb_genres (`int`, *optional*, defaults to 1):
Maximum number of genres that can be used to condition the model.
merged_decoder (`bool`, *optional*, defaults to `True`):
Whether or not the decoder and the encoder inputs are merged. This is used for the separated
encoder-de... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
Multiplier coefficient used to define the hidden dimension of the MLP layers. 0.25 means that 0.25*width of
the model will be used.
music_vocab_size (`int`, *optional*, defaults to 2048):
Number of different music tokens. Should be similar to the `JukeboxVQVAEConfig.nb_discrete_codes`.
... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
res_conv_width (`int`, *optional*, defaults to 128):
Width of the `JukeboxDecoderConvBock` used to upsample the previously sampled audio in the
`JukeboxMusicTokenConditioner`.
res_convolution_multiplier (`int`, *optional*, defaults to 1):
Multiplier used to scale the `hidden_... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
res_strides_t (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
Striding used in the audio conditioning network
resid_dropout (`int`, *optional*, defaults to 0):
Residual dropout used in the attention pattern.
sampling_rate (`int`, *optional*, defaults to 44100):
Sa... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
model_type = "jukebox_prior"
attribute_map = {
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
} | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
def __init__(
self,
act_fn="quick_gelu",
level=0,
alignment_head=2,
alignment_layer=68,
attention_multiplier=0.25,
attention_pattern="enc_dec_with_lyrics",
attn_dropout=0,
attn_res_scale=False,
blocks=64,
conv_res_scale=None,
... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
res_dilation_growth_rate=1,
res_downs_t=[3, 2, 2],
res_strides_t=[2, 2, 2],
resid_dropout=0,
sampling_rate=44100,
spread=None,
timing_dims=64,
zero_out=False,
**kwargs,
):
self.act_fn = act_fn
self.alignment_head = alignment_head
... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
self.is_encoder_decoder = is_encoder_decoder
self.lyric_vocab_size = lyric_vocab_size
self.level = level
self.mask = mask
self.max_duration = max_duration
self.max_nb_genres = max_nb_genres
self.merged_decoder = merged_decoder
self.metadata_conditioning = metadata... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
self.sampling_rate = sampling_rate
self.spread = spread
self.timing_dims = timing_dims
self.hidden_size = hidden_size
self.zero_out = zero_out | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
@classmethod
def from_pretrained(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], level=0, **kwargs
) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the prior config d... | 10,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
class JukeboxVQVAEConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`JukeboxVQVAE`]. It is used to instantiate a
`JukeboxVQVAE` according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a... | 10,355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
Args:
act_fn (`str`, *optional*, defaults to `"relu"`):
Activation function of the model.
nb_discrete_codes (`int`, *optional*, defaults to 2048):
Number of codes of the VQVAE.
commit (`float`, *optional*, defaults to 0.02):
Commit loss multiplier.
con... | 10,355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
Used in the codebook update, exponential moving average coefficient. For more detail refer to Appendix A.1
of the original [VQVAE paper](https://arxiv.org/pdf/1711.00937v2.pdf)
multipliers (`List[int]`, *optional*, defaults to `[2, 1, 1]`):
Depth and width multipliers used for each level... | 10,355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
Dilation cycle value used in the `JukeboxResnet`. If an int is used, each new Conv1 block will have a depth
reduced by a power of `res_dilation_cycle`.
res_dilation_growth_rate (`int`, *optional*, defaults to 3):
Resnet dilation growth rate used in the VQVAE (dilation_growth_rate ** dept... | 10,355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
""" | 10,355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
model_type = "jukebox_vqvae"
def __init__(
self,
act_fn="relu",
nb_discrete_codes=2048,
commit=0.02,
conv_input_shape=1,
conv_res_scale=False,
embed_dim=64,
hop_fraction=[0.125, 0.5, 0.5],
levels=3,
lmu=0.99,
multipliers=[2, 1,... | 10,355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
# VQVAE parameters (all used)
self.levels = levels
self.embed_dim = embed_dim
self.nb_discrete_codes = nb_discrete_codes
self.res_conv_width = res_conv_width
self.res_conv_depth = res_conv_depth
self.res_convolution_multiplier = res_convolution_multiplier
self.res... | 10,355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
# get the text config dict if we are loading from CLIPConfig
if config_dict.get("model_type") == "jukebox":
config_dict = config_dict["vqvae_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
... | 10,355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
class JukeboxConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`JukeboxModel`].
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. Insta... | 10,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
Args:
vqvae_config (`JukeboxVQVAEConfig`, *optional*):
Configuration for the `JukeboxVQVAE` model.
prior_config_list (`List[JukeboxPriorConfig]`, *optional*):
List of the configs for each of the `JukeboxPrior` of the model. The original architecture uses 3 priors.
nb_prio... | 10,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
layer. The timing embedding layer converts the absolute and relative position in the currently sampled
audio to a tensor of length `timing_dims` that will be added to the music tokens.
min_duration (`int`, *optional*, defaults to 0):
Minimum duration of the audios to generate
max... | 10,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
Example:
```python
>>> from transformers import JukeboxModel, JukeboxConfig
>>> # Initializing a Jukebox configuration
>>> configuration = JukeboxConfig()
>>> # Initializing a model from the configuration
>>> model = JukeboxModel(configuration)
>>> # Accessing the model configuration
... | 10,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
self.vqvae_config = JukeboxVQVAEConfig(**vqvae_config)
if prior_config_list is not None:
self.prior_configs = [JukeboxPriorConfig(**prior_config) for prior_config in prior_config_list]
else:
self.prior_configs = []
for prior_idx in range(nb_priors):
pr... | 10,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
# Metadata conditioning
self.max_nb_genres = max_nb_genres
self.sampling_rate = sampling_rate
self.timing_dims = timing_dims
self.min_duration = min_duration
self.max_duration = max_duration
self.metadata_conditioning = metadata_conditioning
super().__init__(**kw... | 10,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
def to_dict(self):
# Override the default to_dict to apply to_dict to the list of prior configs.
result = super().to_dict()
result["prior_config_list"] = [config.to_dict() for config in result.pop("prior_configs")]
return result | 10,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py |
class JukeboxTokenizer(PreTrainedTokenizer):
"""
Constructs a Jukebox tokenizer. Jukebox can be conditioned on 3 different inputs :
- Artists, unique ids are associated to each artist from the provided dictionary.
- Genres, unique ids are associated to each genre from the provided dictionary.
... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
>>> tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
>>> tokenizer("Alan Jackson", "Country Rock", "old town road")["input_ids"]
[tensor([[ 0, 0, 0, 6785, 546, 41, 38, 30, 76, 46, 41, 49,
40, 76, 44, 41, 27, 30]]), tensor([[ 0, 0, 0, 1... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
Args:
artists_file (`str`):
Path to the vocabulary file which contains a mapping between artists and ids. The default file supports
both "v2" and "v3"
genres_file (`str`):
Path to the vocabulary file which contain a mapping between genres and ids.
lyrics_file ... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
""" | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
artists_file,
genres_file,
lyrics_file,
version=["v3", "v2", "v2"],
max_n_lyric_tokens=512,
n_genres=5,
unk_token="<|endoftext|>",
... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
oov = r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
if len(self.lyrics_encoder) == 79:
oov = oov.replace(r"\-'", r"\-+'")
self.out_of_vocab = regex.compile(oov)
self.artists_decoder = {v: k for k, v... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
def get_vocab(self):
return {
"artists_encoder": self.artists_encoder,
"genres_encoder": self.genres_encoder,
"lyrics_encoder": self.lyrics_encoder,
}
def _convert_token_to_id(self, list_artists, list_genres, list_lyrics):
"""Converts the artist, genre an... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
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