text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_sta... | 2,971 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
if self.is_decoder:
# 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 uni-directional self-attention (d... | 2,971 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
... | 2,971 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = la... | 2,971 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_we... | 2,971 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
... | 2,971 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusEncoderLayer(nn.Module):
def __init__(self, config: BigBirdPegasusConfig, seed=None):
super().__init__()
self.attention_type = config.attention_type
self.embed_dim = config.d_model
self.self_attn = BigBirdPegasusEncoderAttention(config, seed=seed)
self.sel... | 2,972 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
output_attentions: bool = False,
):... | 2,972 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
self_attention_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=layer_head_mask,
output_attentions=output_attentions,
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
... | 2,972 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hi... | 2,972 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusDecoderLayer(nn.Module):
def __init__(self, config: BigBirdPegasusConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BigBirdPegasusDecoderAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | 2,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = BigBirdPegasusDecoderAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
bias=config.use_bias,
)
self... | 2,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer.forward
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]... | 2,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatT... | 2,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
hidden_states = self.self_attn_layer_norm(hidden_states) | 2,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states) | 2,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self... | 2,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusClassificationHead(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,... | 2,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusPreTrainedModel(PreTrainedModel):
config_class = BigBirdPegasusConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["BigBirdPegasusEncoderLayer", "BigBirdPegasusDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_... | 2,975 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
}
retu... | 2,975 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusEncoder(BigBirdPegasusPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BigBirdPegasusEncoderLayer`].
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([BigBirdPegasusEncoderLayer(config, seed=i) f... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Opti... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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**,
... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=hidden_states.device)
attention_mask = attention_mask.long() | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
# in order to use block_sparse attention, sequence_length has to be at least
# bigger than all global attentions: 2 * block_size
# + sliding tokens: 3 * block_size
# + random tokens: 2 * num_random_blocks * block_size
max_tokens_to_attend = (5 + 2 * self.config.num_random_blocks) * self.... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
f"= {max_tokens_to_attend} with config.block_size "
f"= {self.config.block_size}, config.num_random_blocks "
f"= {self.config.num_random_blocks}. "
"Changing attention type to 'original_full'..."
)
self.set_attention_type("original_full") | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
if self.attention_type == "block_sparse":
padding_len, hidden_states, attention_mask = self._pad_to_block_size(hidden_states, attention_mask)
else:
padding_len = 0
# expand attention_mask
if self.attention_type == "original_full":
# [bsz, seq_len] -> [bsz, 1,... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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,
... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
to_mask=to_mask,
from_blocked_mask=blocked_encoder_mask,
to_blocked_mask=blocked_encoder_mask,
output_attentions=output_attentions,
) | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layernorm_embedding(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if padding_... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if valu... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask):
"""
Create 3D attention mask from a 2D tensor mask.
Args:
from_blocked_mask: 2D Tensor of shape [batch_size,
from_seq_length//from_block_size, from_block_size].
to_b... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
blocked_encoder_mask = attention_mask.view(batch_size, seq_length // block_size, block_size)
band_mask = create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask)
from_mask = attention_mask.view(batch_size, 1, seq_length, 1)
to_mask = attention_mask.view(batch_size, 1, 1, seq_len... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
padding_len = (block_size - seq_len % block_size) % block_size
if padding_len > 0:
logger.warning_once(
f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of "
f"`config.block_size`: {block_size}"
)
... | 2,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusDecoder(BigBirdPegasusPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BigBirdPegasusDecoderLayer`]
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, c... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList([BigBirdPegasusDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_mode... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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*):
... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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
... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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 decoder to avoid performin... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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. | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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_shape = input_ids.size()
input_ids... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
# 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, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=in... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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):
... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
past_key_value = past_key_values[idx] if past_key_values is not None else None | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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,
... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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],)
hid... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusModel(BigBirdPegasusPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BigBirdPegasusConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embe... | 2,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
def get_encoder(self):
return self.encoder
def get_decoder(self):
re... | 2,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
# Copied from transformers.models.bart... | 2,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqModelOutput]:
# different to other ... | 2,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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,
... | 2,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
def __init__(self, conf... | 2,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
self._resize_final_logits_bias(new_embeddings.weight.shape[0])
return new_embeddings
def _re... | 2,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BIGBIRD_PEGASUS_GENERATION_EXAMPLE)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask... | 2,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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
... | 2,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
masked_lm_loss = None
if labels is not None:
labels = labels.to(lm_logits.device)
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
... | 2,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
... | 2,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusForSequenceClassification(BigBirdPegasusPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BigBirdPegasusConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = BigBirdPegasusModel(confi... | 2,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
# Copied from transformers.models.bart.modeling_bart.BartForSequenceClass... | 2,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqSequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional... | 2,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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 ==... | 2,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel):
_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.mode... | 2,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
@add_start_docstrings_to_model_forward(BIGBIRD_PEGASUS_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
# Copied from transformers.models.bart.modeling_bart.BartForQuestionA... | 2,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Seq2SeqQuestionAnsweringModelOutput]:
r"""... | 2,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if start_positions is not None and en... | 2,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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:
... | 2,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusDecoderWrapper(BigBirdPegasusPreTrainedModel):
"""
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().__in... | 2,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel, 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.mo... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it. | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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*):
... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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,... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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, ... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
>>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> model = BigBirdPegasusForCausalLM.from_pretrained(
... "google/bigbird-pegasus-large-arxiv", add_cross_attention=False
... )
>>> assert model.config.is_decoder, f"{model.__class__} has to be confi... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
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