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Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic toke... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
x_sep_token (`str`, *optional*, defaults to `"[X_SEP]"`):
Special second separator token, which can be generated by [`ProphetNetForCondi... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
def __init__(
self,
vocab_file: str,
do_lower_case: Optional[bool] = True,
do_basic_tokenize: Optional[bool] = True,
never_split: Optional[Iterable] = None,
unk_token: Optional[str] = "[UNK]",
sep_token: Optional[str] = "[SEP]",
x_sep_token: Optional[str] ... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
x_sep_token=x_sep_token,
pad_token=pad_token,
mask_token=mask_token,
... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
def _tokenize(self, text):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
# If the token is part of the never_split set
if token in self.basic_tokenizer.never_split:
... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
def convert_tokens_to_string(self, tokens: str):
"""Converts a sequence of tokens (string) in a single string."""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def get_special_tokens_mask(
self,
token_ids_0: List[int],
token_ids_1: Option... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=Tru... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`Li... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has t... | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
class ProphetNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ProphetNetModel`]. It is used to instantiate a
ProphetNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yie... | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
Args:
activation_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for activations inside the fully connected layer.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder... | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
Number of encoder layers.
num_encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the `intermediate` (often named feed-... | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight ma... | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
token.
num_buckets (`int`, *optional*, defaults to 32)
The number of buckets to use for each attention layer. This is for relative position calculation. See the
[T5 pap... | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
smoothing is performed.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
""" | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
model_type = "prophetnet"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_attention_heads": "num_encoder_attention_heads",
} | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
def __init__(
self,
activation_dropout: Optional[float] = 0.1,
activation_function: Optional[Union[str, Callable]] = "gelu",
vocab_size: Optional[int] = 30522,
hidden_size: Optional[int] = 1024,
encoder_ffn_dim: Optional[int] = 4096,
num_encoder_layers: Optional[i... | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
disable_ngram_loss: Optional[bool] = False,
eps: Optional[float] = 0.0,
use_cache: Optional[bool] = True,
pad_token_id: Optional[int] = 0,
bos_token_id: Optional[int] = 1,
eos_token_id: Optional[int] = 2,
**kwargs,
):
self.vocab_size = vocab_size
self.... | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
# parameters for prophetnet
self.ngram = ngram
self.num_buckets = num_buckets
self.relative_max_distance = relative_max_distance
self.disable_ngram_loss = disable_ngram_loss
self.eps = eps
# 3 Types of Dropout
self.attention_dropout = attention_dropout
se... | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
@num_hidden_layers.setter
def num_hidden_layers(self, value):
raise NotImplementedError(
"This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
" `num_decoder_layers`."
) | 9,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py |
class ProphetNetSeq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs. | 9,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, config.vocab_size)`):
Prediction scores of the main stream language modeling head ... | 9,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `conf... | 9,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.
decoder_ngram_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTenso... | 9,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_ngram_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
T... | 9,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at... | 9,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_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 sha... | 9,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
logits_ngram: Optional[torch.FloatTensor] = None
past_key_values: Optional[Tuple[torch.FloatTensor]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_ngram_hidden_states: Optional[Tuple[torch.Flo... | 9,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
@property
def decoder_cross_attentions(self):
warnings.warn(
"`decoder_cross_attentions` is deprecated and will be removed soon. Please use `cross_attentions`"
" instead.",
FutureWarning,
)
return self.cross_attentions | 9,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetSeq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`):
... | 9,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
last_hidden_state_ngram (`torch.FloatTensor` of shape `(batch_size,ngram * decoder_sequence_length, config.vocab_size)`, *optional*):
Sequence of predict stream hid... | 9,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `conf... | 9,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.
decoder_ngram_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTenso... | 9,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_ngram_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
T... | 9,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at... | 9,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_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 sha... | 9,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
last_hidden_state: torch.FloatTensor
last_hidden_state_ngram: Optional[torch.FloatTensor] = None
past_key_values: Optional[Tuple[torch.FloatTensor]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_ngram_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_... | 9,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetDecoderModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`):
Sequence of main st... | 9,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
last_hidden_state_ngram (`torch.FloatTensor` of shape `(batch_size, ngram * decoder_sequence_length, config.vocab_size)`):
Sequence of predict stream hidden-states ... | 9,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see `past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.outpu... | 9,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.
ngram_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one ... | 9,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
ngram_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of ... | 9,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the
"""
last_hidden_state: torch.FloatTensor
last_hidden_state_ngram: Optional[torch.FloatTensor] = None
past_key_values: Optional[Tuple[torch.FloatTensor]] =... | 9,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetDecoderLMOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). | 9,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, config.vocab_size)`):
Prediction scores of the main stream language modeling head ... | 9,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see `past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.outpu... | 9,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Hidden-states of main stream of the decoder at the output of each layer plus the initial embedding outputs.
ngram_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one ... | 9,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
ngram_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of ... | 9,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
compute the weighted average in the
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
logits_ngram: Optional[torch.FloatTensor] = None
past_key_values: Option... | 9,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetPreTrainedModel(PreTrainedModel):
config_class = ProphetNetConfig
base_model_prefix = "prophetnet"
supports_gradient_checkpointing = True
def _init_weights(self, module):
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.init_std)... | 9,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
assert decoder_start_token_id is not None, (
"self.model.config.decoder_start_token_id has to be defined. In ProphetNet it is usually set to the"
" pad_token_id. See ProphetNet docs for more information"
)
# shift inputs to the right
shifted_input_ids = input_ids.new_zer... | 9,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetPositionalEmbeddings(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size. Padding ids are ignored by either offsetting
based on padding_idx or by setting padding_idx to None and ensuring that the appropriate position ids are passed to
the forward functi... | 9,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if position_ids is None:
if past_key_values is not None:
# position_ids is the same for every token when decoding a single step
# Without the int() cast, it doesn't work in some cases when exporting to ONNX
prev_num_input_ids = past_key_values[0][0].shape[2]
... | 9,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# make sure position_ids are not bigger then max_length
position_ids = position_ids.clamp(0, self.max_length - 1)
return super().forward(position_ids), position_ids
def _forward(self, position_ids):
return super().forward(position_ids) | 9,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config: ProphetNetConfig,
num_attn_heads: int,
):
super().__init__()
hidden_size = config.hidden_size
self.attention_dropout = confi... | 9,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_attn_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states,
key_value_states: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = N... | 9,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# previous time steps are cached - no need to recompute key and value if they are static
query_states = self.query_proj(hidden_states) / (self.head_dim**0.5)
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
... | 9,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if is_cross_attention:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if encoder bi-directional self-at... | 9,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# project states into the correct shape
proj_shape = (batch_size, self.num_attn_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, batch_size).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_le... | 9,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
expected_shape = (batch_size, self.num_attn_heads, 1, src_len)
if attention_mask is not None and attention_mask.size() != expected_shape:
raise ValueError(f"Attention mask should have size {expected_shape}, but is {attention_mask.size()}")
if attention_mask is not None: # don't attend to pa... | 9,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if layer_head_mask is not None:
assert layer_head_mask.size() == (self.num_attn_heads,), (
f"Head mask for a single layer should be of size {(self.num_attn_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1... | 9,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
attn_probs = nn.functional.dropout(
attn_weights,
p=self.attention_dropout,
training=self.training,
)
attn_output = torch.einsum("bsij,bsjk->bsik", attn_probs, value_states)
expected_shape = (batch_size, self.num_attn_heads, tgt_len, self.head_dim)
if ... | 9,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetFeedForward(nn.Module):
"""
This is the residual two feed-forward layer block based on the original Transformer implementation.
"""
def __init__(self, config: ProphetNetConfig, ffn_dim: int):
super().__init__()
self.activation_fn = ACT2FN[config.activation_function]
... | 9,331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetNgramSelfAttention(nn.Module):
def __init__(self, config: ProphetNetConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.num_buckets = config.num_buckets
self.relative_max_distance = config.relative_max_distance
self.num_attn_heads = config.... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# out projection
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
# rel position embeddings
self.relative_pos_embeddings = nn.Linear(config.hidden_size, self.num_buckets * self.num_attn_heads)
# for onnx runtime
self.onnx_trace = False
def _shape(self, ten... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def forward(
self,
hidden_states,
past_key_value: Optional[Tuple[Tensor]] = None,
attention_mask=None,
layer_head_mask=None,
extended_predict_attention_mask=None,
main_relative_position_buckets=None,
predict_relative_position_buckets=None,
position... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# reshape
query_states = self._shape(query_states, ngram_sequence_length, batch_size)
key_states = self._shape(key_states, -1, batch_size)
value_states = self._shape(value_states, -1, batch_size)
proj_shape = (batch_size, self.num_attn_heads, -1, self.head_dim)
query_states = qu... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
main_hidden_states, hidden_states_predict_list = hidden_states_list[0], hidden_states_list[1:]
main_query_states, predict_query_states_list = query_states_list[0], query_states_list[1:]
main_key_states, predict_key_states_list = key_states_list[0], key_states_list[1:]
main_value_states, predict_... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# MAIN-STREAM
# main attn weights
# [batch_size, number_heads, sequence_length, head_dimesion]
# x [batch_size, number_heads, head_dimesion, sequence_length]
# -> [batch_size, number_heads, sequence_length, sequence_length]
main_attn_weights = torch.einsum("bntc,bncs->bnts", main... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if layer_head_mask is not None:
assert layer_head_mask.size() == (self.num_attn_heads,), (
f"Head mask for a single layer should be of size {(self.num_attn_heads,)}, but is"
f" {layer_head_mask.size()}"
)
main_attn_probs = layer_head_mask.view(1, -1, 1... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
main_attn_probs = nn.functional.dropout(main_attn_probs, p=self.attention_dropout, training=self.training)
# project to attn_output
# [batch_size, number_heads, sequence_length, sequence_length]
# x [batch_size, number_heads, sequence_length, head_dimesion]
# -> [batch_size, number_heads... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# [batch_size, ngram, number_heads, 2*sequence_length, head_dimesion]
predict_key_states = torch.stack([torch.cat([main_key_states, key], 2) for key in predict_key_states_list], 1)
# [batch_size, sequence_length, ngram, hidden_size]
predict_hidden_states = torch.stack(hidden_states_predict_list... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# retrieve relative position embeddings for each layer -> see paper for more details
# [batch_size, ngram, number_heads, sequence_length, predict_relative_pos_embeddings]
predict_relative_pos_embeddings = self.get_predict_relative_pos_embeddings(
predict_hidden_states, predict_attn_weights, ... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if extended_predict_attention_mask is not None:
# Permuting Predict attention mask to [batch_size, ngram, number_heads, sequence_length, 2*sequence_length]
extended_predict_attention_mask = extended_predict_attention_mask.permute(0, 2, 1, 3, 4)
extended_predict_attention_mask = exten... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
predict_attn_probs = nn.functional.dropout(
predict_attn_probs, p=self.attention_dropout, training=self.training
)
# project to attention output
# [batch_size, ngram, number_heads, sequence_length, 2*sequence_length]
# x [batch_size, ngram, number_heads, 2*sequence_length, he... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# concat to single attn output
# [batch_size, (1+ngram)*sequence_length, hidden_size]
attn_output = torch.cat([main_attn_output, predict_attn_output], 1).view(batch_size, -1, hidden_size)
# reshape into better form for `config.output_attentions`
main_attn_probs = main_attn_probs.view(bat... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def get_main_relative_pos_embeddings(
self, hidden_states, attn_weights, position_ids, main_relative_position_buckets
):
# input hidden_states [batch_size, sequence_length, hidden_size]
# input attn_weights [batch_size, num_heads, sequence_length, sequence_length]
# input position_id... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
relative_positions = relative_positions - position_ids.unsqueeze(0).repeat(batch_size, sequence_length, 1)
main_relative_position_buckets = compute_relative_buckets(
self.num_buckets, self.relative_max_distance, relative_positions, False
) | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# [batch_size, sequence_length, num_buckets * num_heads]
rel_pos_embeddings = self.relative_pos_embeddings(hidden_states)
rel_pos_embeddings = rel_pos_embeddings.view(
rel_pos_embeddings.shape[:2] + (self.num_buckets, self.num_attn_heads)
)
rel_pos_embeddings = rel_pos_embedd... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
main_relative_position_buckets = main_relative_position_buckets.repeat(1, self.num_attn_heads, 1)
# [batch_size * num_heads * sequence_length, sequence_length]
main_relative_position_buckets = main_relative_position_buckets.view(
-1, main_relative_position_buckets.shape[-1]
)
... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def get_predict_relative_pos_embeddings(
self, hidden_states, attn_weights, position_ids, predict_relative_position_buckets
):
# input hidden_states [batch_size, sequence_length, ngram, hidden_size]
# input attn_weights [batch_size, ngram, num_heads, sequence_length, 2*sequence_length]
... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if predict_relative_position_buckets is None:
key_sequence_length = attn_weights.shape[-1]
assert (
position_ids[0][0] == key_sequence_length - 1
), "`position_ids` are incorrect. They should be of the format 1 2 3 4 5 ... (key_sequence_length - 1)"
relati... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# [batch_size, ngram, sequence_length, hidden_size]
hidden_states = hidden_states.transpose(1, 2)
rel_pos_embeddings = self.relative_pos_embeddings(hidden_states) | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# [batch_size, ngram, sequence_length, num_buckets, num_heads]
rel_pos_embeddings = rel_pos_embeddings.view(
hidden_states.shape[:-1] + (self.num_buckets, self.num_attn_heads)
)
rel_pos_embeddings = rel_pos_embeddings.permute(0, 2, 1, 4, 3)
# [batch_size * ngram * sequence_le... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
predict_relative_pos_embeddings = torch.gather(
rel_pos_embeddings, dim=1, index=predict_relative_position_buckets
)
# [batch_size, gram, num_heads, sequence_length, -1]
predict_relative_pos_embeddings = predict_relative_pos_embeddings.view(
batch_size, self.ngram, self.... | 9,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetEncoderLayer(nn.Module):
"""
Encoder block for Prophetnet
"""
def __init__(self, config: ProphetNetConfig):
super().__init__()
# 1st residual block
self.self_attn = ProphetNetAttention(config, config.num_encoder_attention_heads)
self.self_attn_layer_norm ... | 9,333 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def forward(
self,
hidden_states,
attention_mask,
layer_head_mask,
output_attentions: bool = False,
):
# 1st residual block
attention_output, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
... | 9,333 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetDecoderLayer(nn.Module):
"""
Decoder block for Prophetnet
"""
def __init__(self, config: ProphetNetConfig):
super().__init__()
# 1st residual block
self.self_attn = ProphetNetNgramSelfAttention(config)
self.self_attn_layer_norm = LayerNorm(config.hidden_s... | 9,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
def forward(
self,
hidden_states,
attention_mask=None,
encoder_hidden_states=None,
encoder_attn_mask=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
extended_predict_attention_mask=None,
main_relative_position_buckets=None,
pre... | 9,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
extended_predict_attention_mask=extended_predict_attention_mask,
main_relative_position_buckets=main_relative_position_buckets,
predict_relative_position_buckets=predict_relative_position_buckets,
position_ids=position_ids,
)
hidden_states = self.self_attn_layer_norm(... | 9,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.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
cross_attn_weights = None
if encoder_hidden_states is not None:
# 2nd residual block
attention_outpu... | 9,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
# 3rd residual block
feed_forward_output = self.feed_forward(hidden_states)
hidden_states = self.feed_forward_layer_norm(feed_forward_output + hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, self_attn_weights_ngram, cross_attn_... | 9,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
class ProphetNetEncoder(ProphetNetPreTrainedModel):
r"""
word_embeddings (`torch.nn.Embeddings` of shape `(config.vocab_size, config.hidden_size)`, *optional*):
The word embedding parameters. This can be used to initialize [`ProphetNetEncoder`] with pre-defined word
embeddings instead of random... | 9,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.word_embeddings
def set_input_embeddings(self, value):
self.word_embeddings = value
@add_start_docstrings_to_model_forward(PROPH... | 9,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
```python
>>> from transformers import AutoTokenizer, ProphetNetEncoder
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
>>> model = ProphetNetEncoder.from_pretrained("patrickvonplaten/prophetnet-large-uncased-standalone")
>>> ... | 9,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
if input_ids is None and inputs_embeds is None:
raise ValueError("Either input_ids or inputs_embeds has to be passed.")
elif input_ids is not None and inputs_embeds is not None:
raise ValueError("Make sure to only pass input_ids or inputs_embeds.")
elif input_ids is not None and ... | 9,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
hidden_states = inputs_embeds + position_embeddings
hidden_states = self.embeddings_layer_norm(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.config.dropout, training=self.training)
encoder_hidden_states = () if output_hidden_states else None
all_attentions =... | 9,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py |
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