text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_l... | 4,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attentio... | 4,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
`(2, decoder_attention_heads, pro_len, head_dim)`.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
... | 4,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_w... | 4,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states | 4,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_st... | 4,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidd... | 4,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim: int,
inner_dim: int,
num_classes: int,
pooler_dropout: float,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)... | 4,090 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpPrompt(nn.Module):
"""Layer-wise prompt for encoder or decoder."""
def __init__(self, config, num_layers, num_heads):
super().__init__()
self.prompt_length = config.prompt_length
self.num_layers = num_layers
self.num_heads = num_heads
self.head_dim = config.d_mo... | 4,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
def forward(self, prompt_ids: torch.Tensor) -> Tuple[torch.Tensor]:
prompt = self.prompt_trans(self.prompt_embedding(prompt_ids))
prompt = prompt.view(self.prompt_length, self.num_layers * 2, self.num_heads, self.head_dim)
prompt = self.dropout(prompt)
prompt = prompt.permute([1, 2, 0, 3... | 4,091 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpPreTrainedModel(PreTrainedModel):
config_class = MvpConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
... | 4,092 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpEncoder(MvpPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`MvpEncoderLayer`].
Args:
config: MvpConfig
embed_tokens (nn.Embedding): output embedding
use_prompt (bool): whether to use prompt
"""
... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
self.embed_positions = MvpLearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([MvpEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(embed_dim)
self.use_prompt = us... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optio... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices in... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.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... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input = input_ids
input_shape = input.shape
... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# layer-wise prompt
if self.use_prompt:
prompt_ids = torch.arange(self.prompt_length).to(self.device)
self_attn_prompt = self.self_attn_prompt(prompt_ids)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_p... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, enco... | 4,093 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpDecoder(MvpPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MvpDecoderLayer`]
Args:
config: MvpConfig
embed_tokens (nn.Embedding): output embedding
use_prompt (bool): whether to use prompt
"""
def __init__(
... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
self.embed_positions = MvpLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList([MvpDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_model)
self.use_p... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cr... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid perfo... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.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)`.
input... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_a... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input = input_ids
input_shape = input_id... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len,... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# layer-wise prompt
if self.use_prompt:
prompt_ids = torch.arange(self.prompt_length).to(self.device)
self_attn_prompt = self.self_attn_prompt(prompt_ids)
cross_attn_prompt = self.cross_attn_prompt(prompt_ids)
if self.gradient_checkpointing and self.training:
... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
past_key_value = past_key_values[idx] if past_key_values is not None else None | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# a... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPa... | 4,094 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpModel(MvpPreTrainedModel):
_keys_to_ignore_on_load_unexpected = ["final_logits_bias"]
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: MvpConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, ... | 4,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
def get_decoder(self):
return self.decoder
def set_lightweight_tuning(self):
assert self.use_prompt, "If you want to use lightweight tuning, make sure that `use_prompt=True`."
self.requires_grad_(False)
self.encoder.self_attn_prompt.requires_grad_(True)
self.decoder.self_at... | 4,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
@add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_ids: torc... | 4,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqModelOutput]:
# different to other models, Mvp automatically creates decoder_input_ids from
# input_ids if no decoder_input_ids are prov... | 4,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
decoder_input_ids = shift_tokens_right(
input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_stat... | 4,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden... | 4,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
... | 4,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_output... | 4,095 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpForConditionalGeneration(MvpPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: MvpConfig):
super().__init__(config)
self.model = MvpModel(config)
self.register_buffe... | 4,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_token... | 4,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
@add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(MVP_CONDITIONAL_GENERATION_EXAMPLE)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[t... | 4,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the m... | 4,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
... | 4,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mas... | 4,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
d... | 4,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(past_state.index_select(0, beam... | 4,096 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpForSequenceClassification(MvpPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: MvpConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = MvpModel(config)
self.classification_head = MvpCla... | 4,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
@add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING)
@add_end_docstrings(MVP_SEQUENCE_CLASSIFICATION_SAMPLE)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
... | 4,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classi... | 4,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if input_ids is None and inputs_embeds is not None:
raise NotImplementedError(
f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decod... | 4,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
raise ValueError("All examples must have the same number of <eos> tokens.")
sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
:, -1, :
]
logits = self.classi... | 4,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.config.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type ==... | 4,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
return Seq2SeqSequenceClassifierOutput(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions... | 4,097 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpForQuestionAnswering(MvpPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.model = MvpModel(config)
... | 4,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
@add_start_docstrings_to_model_forward(MVP_INPUTS_DOCSTRING)
@add_end_docstrings(MVP_QUESTION_ANSWERING_SAMPLE)
def forward(
self,
input_ids: torch.Tensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_... | 4,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]:
r"""
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.
... | 4,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if start_positions is not None and end_positions is not None:
use_cache = False | 4,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_att... | 4,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.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:
... | 4,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if not return_dict:
output = (
start_logits,
end_logits,
) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return Seq2SeqQuestionAnsweringModelOutput(
loss=total_loss,
start_logits=st... | 4,098 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpDecoderWrapper(MvpPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
s... | 4,099 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpForCausalLM(MvpPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MvpDecoderWrappe... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
def set_lightweight_tuning(self):
self.model.set_lightweight_tuning()
self.lm_head.requires_grad_(False) | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_att... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it. | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
[What are attention masks?](../glossary#attention-mask)
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... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0,... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, ... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.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)`.
label... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
ou... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> list(logits.shape)
[1, 8, 50267]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_atte... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs... | 4,100 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" MVP tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer,
using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiec... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
</Tip>
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When bu... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequenc... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the mode... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = MvpTokenizer | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
tokenizer_component = "post_processor"
tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
if tokenizer_component_instance:
state = json.loads(tokenizer_component_instance... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
if changes_to_apply:
component_class = getattr(processors, state.pop("type"))
new_value = component_class(**state)
setattr(self.backend_tokenizer, tokenizer_component, new_value)
@property
def mask_token(self) -> str:
"""
`str`: Mask token, to use... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
This is needed to preserve backward compatibility with all the previously used models based on Mvp.
"""
# Mask token behave like a normal word, i.e. include the space before it
# So we set lstrip to True
value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else ... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*args, **kwargs)
def save_voc... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. MVP does not
make use of token type ids, therefore a list... | 4,101 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py |
class MvpConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MvpModel`]. It is used to instantiate a MVP model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configur... | 4,102 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/configuration_mvp.py |
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