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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... | 10,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 | 10,116 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetSeq2SeqModelOutput(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)`):
... | 10,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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_... | 10,117 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetDecoderModelOutput(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... | 10,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 ... | 10,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 ... | 10,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 ... | 10,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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]] =... | 10,118 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetDecoderLMOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). | 10,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 ... | 10,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 ... | 10,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 ... | 10,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,119 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetPreTrainedModel(PreTrainedModel):
config_class = XLMProphetNetConfig
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.ini... | 10,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
assert decoder_start_token_id is not None, (
"self.model.config.decoder_start_token_id has to be defined. In XLMProphetNet it is usually set to the"
" pad_token_id. See XLMProphetNet docs for more information"
)
# shift inputs to the right
shifted_input_ids = input_ids.n... | 10,120 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetPositionalEmbeddings(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 fun... | 10,121 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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]
... | 10,121 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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) | 10,121 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config: XLMProphetNetConfig,
num_attn_heads: int,
):
super().__init__()
hidden_size = config.hidden_size
self.attention_dropout =... | 10,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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]
... | 10,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 ... | 10,122 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetFeedForward(nn.Module):
"""
This is the residual two feed-forward layer block based on the original Transformer implementation.
"""
def __init__(self, config: XLMProphetNetConfig, ffn_dim: int):
super().__init__()
self.activation_fn = ACT2FN[config.activation_function... | 10,123 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetNgramSelfAttention(nn.Module):
def __init__(self, config: XLMProphetNetConfig):
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 = c... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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_... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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, ... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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
) | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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]
)
... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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]
... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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) | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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.... | 10,124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetEncoderLayer(nn.Module):
"""
Encoder block for XLMProphetnet
"""
def __init__(self, config: XLMProphetNetConfig):
super().__init__()
# 1st residual block
self.self_attn = XLMProphetNetAttention(config, config.num_encoder_attention_heads)
self.self_attn... | 10,125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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,
... | 10,125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetDecoderLayer(nn.Module):
"""
Decoder block for XLMProphetnet
"""
def __init__(self, config: XLMProphetNetConfig):
super().__init__()
# 1st residual block
self.self_attn = XLMProphetNetNgramSelfAttention(config)
self.self_attn_layer_norm = LayerNorm(con... | 10,126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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(... | 10,126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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... | 10,126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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_... | 10,126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetEncoder(XLMProphetNetPreTrainedModel):
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 [`XLMProphetNetEncoder`] with pre-defined word
embeddings instead ... | 10,127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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(XLM_P... | 10,127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
```python
>>> from transformers import AutoTokenizer, XLMProphetNetEncoder
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = XLMProphetNetEncoder.from_pretrained("patrickvonplaten/prophetnet-large-uncased-... | 10,127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 ... | 10,127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_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 =... | 10,127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
extended_attention_mask,
(head_mask[idx] if head_mask is not None else None),
... | 10,127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
if not return_dict:
return tuple(v for v in [hidden_states, encoder_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_hidden_states, attentions=all_attentions
) | 10,127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetDecoder(XLMProphetNetPreTrainedModel):
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 [`XLMProphetNetEncoder`] with pre-defined word
embeddings instead ... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
self.word_embeddings = (
word_embeddings
if word_embeddings is not None
else nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
)
self.position_embeddings = XLMProphetNetPositionalEmbeddings(config)
self.ngram_embeddings = nn... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
@add_start_docstrings_to_model_forward(XLM_PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=XLMProphetNetDecoderModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = N... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.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... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **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 precomp... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, XLMProphetNetDecoder
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xproph... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
>>> last_hidden_states = outputs.last_hidden_state
```"""
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
batch_size, sequence_length = inputs_embeds.shape[:2]
main_stream_pos_embed, position_ids = self.position_embeddings(
(batch_size, sequence_length),
device=inputs_embeds.device,
past_key_values=past_key_values,
)
if past_key_values is not None:
m... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
# prepare attention mask
if past_key_values is not None:
assert (
hidden_states.size(1) == 1
), "At the moment `use_cache` is only supported for `decoder_input_ids` of length 1"
ngram_hidden_states = [
(ngram_embeddings[ngram - 1] + predicting... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
# prepare encoder attention mask
if encoder_attention_mask is not None:
extended_encoder_attention_mask = (
1.0 - encoder_attention_mask[:, None, None, :].repeat(1, self.config.num_decoder_attention_heads, 1, 1)
) * torch.finfo(self.dtype).min
extended_encoder... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
all_main_stream_attns = () if output_attentions else None
all_ngram_stream_attns = () if output_attentions else None
all_cross_attns = () if output_attentions and self.config.add_cross_attention else None
if self.gradient_checkpointing and self.training:
if use_cache:
... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.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:
assert attn_mask.size()[0] == (len(self.layers)), (
... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
extended_attention_mask,
encoder_hidden_states,
extended_encoder... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
encoder_hidden_states=encoder_hidden_states,
encoder_attn_mask=extended_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_he... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
hidden_states = layer_outputs[0]
if use_cache:
present_key_values += (layer_outputs[4 if output_attentions else 1],)
if output_attentions:
all_main_stream_attns += (layer_outputs[1],)
all_ngram_stream_attns += (layer_outputs[2],)
... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
if not return_dict:
return tuple(
v
for v in [
last_hidden_state,
last_hidden_state_ngram,
present_key_values,
all_main_stream_hidden_states,
all_ngram_stream_hidden_states,
... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
def compute_buffered_relative_buckets(self, position_ids):
batch_size, sequence_length = position_ids.shape
position_ids = torch.arange(1, self.max_target_positions).to(position_ids.device).repeat(1, 1)
main_relative_buckets, predict_relative_buckets = compute_all_stream_relative_buckets(
... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
def prepare_attention_mask(self, hidden_states, attention_mask):
batch_size, seq_length = hidden_states.shape[:2]
# get causal mask
causal_mask = torch.full(
(seq_length, seq_length),
torch.finfo(hidden_states.dtype).min,
dtype=hidden_states.dtype,
... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
def prepare_predict_attention_mask(self, hidden_states, attention_mask):
batch_size, seq_length = hidden_states.shape[:2]
# get causal mask
predict_causal_mask = ngram_attention_bias(
self.max_target_positions, self.ngram, hidden_states.device, hidden_states.dtype
)
... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
# add usual attention mask
if attention_mask is not None:
extended_attention_mask = (1.0 - attention_mask[:, None, None, None, :]) * torch.finfo(self.dtype).min
extended_attention_mask = extended_attention_mask.expand(
(batch_size, self.config.num_decoder_attention_heads,... | 10,128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetModel(XLMProphetNetPreTrainedModel):
_tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight"]
def __init__(self, config: XLMProphetNetConfig):
super().__init__(config)
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, ... | 10,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
def set_input_embeddings(self, value):
self.word_embeddings = value
self.encoder.word_embeddings = self.word_embeddings
self.decoder.word_embeddings = self.word_embeddings
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder... | 10,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
@add_start_docstrings_to_model_forward(XLM_PROPHETNET_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=XLMProphetNetSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
... | 10,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
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