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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_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | 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 (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.reshape(*proj_shape)
value_states = value_states.reshape(*proj_shape) | 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"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1) | 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 = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) | 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_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
) | 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.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value | 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.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim) | 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,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states) | 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,
from_blocked_mask=from_blocked_mask,
to_blocked_mask=to_blocked_mask,
)
hidden_states = self_attention_outputs[0]
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states | 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 = (hidden_states,)
if output_attentions:
outputs += (self_attention_outputs[1],)
return outputs
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 value == self.attention_type:
return
self.attention_type = value
self.self_attn.set_attention_type(value) | 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,
dropout=config.attention_dropout,
is_decoder=True,
bias=config.use_bias,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout | 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.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim) | 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] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`): | 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.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states | 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_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states | 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_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value | 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.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs | 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, inner_dim)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states | 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_param_buffer_assignment = False
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_() | 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,
}
return dummy_inputs | 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
"""
def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.attention_type = config.attention_type
self.block_size = config.block_size
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.embed_tokens = BigBirdPegasusScaledWordEmbedding(
config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale
) | 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) for i in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(embed_dim)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init() | 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: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details. | 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**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask) | 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 into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
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.
""" | 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 else self.config.use_return_dict | 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)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) | 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.config.block_size
if self.attention_type == "block_sparse" and input_shape[1] <= max_tokens_to_attend:
# change attention_type from block_sparse to original_full
sequence_length = input_shape[1]
logger.warning(
"Attention type 'block_sparse' is not possible if sequence_length: "
f"{sequence_length} <= num global tokens: 2 * config.block_size "
"+ min. num sliding tokens: 3 * config.block_size "
"+ config.num_random_blocks * config.block_size "
"+ additional buffer: config.num_random_blocks * config.block_size " | 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, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
blocked_encoder_mask = band_mask = from_mask = to_mask = None
elif self.attention_type == "block_sparse":
blocked_encoder_mask, band_mask, from_mask, to_mask = self.create_masks_for_block_sparse_attn(
attention_mask, self.block_size
)
attention_mask = None
else:
raise ValueError(
f"attention_type can either be original_full or block_sparse, but is {self.attention_type}"
) | 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(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True | 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,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
band_mask,
from_mask,
to_mask,
blocked_encoder_mask,
blocked_encoder_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
band_mask=band_mask,
from_mask=from_mask, | 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_len > 0:
# unpad `sequence_output` because the calling function is expecting a length == input_ids.size(1)
hidden_states = hidden_states[:, :-padding_len]
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
self.encoder_o = hidden_states
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
) | 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 value == self.attention_type:
return
self.attention_type = value
for layer in self.layers:
layer.set_attention_type(value)
@staticmethod # Copied from transformers.models.big_bird.modeling_big_bird.BigBirdModel.create_masks_for_block_sparse_attn
def create_masks_for_block_sparse_attn(attention_mask: torch.Tensor, block_size: int):
batch_size, seq_length = attention_mask.size()
if seq_length % block_size != 0:
raise ValueError(
f"Sequence length must be multiple of block size, but sequence length is {seq_length}, while block"
f" size is {block_size}."
) | 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_blocked_mask: int32 Tensor of shape [batch_size,
to_seq_length//to_block_size, to_block_size].
Returns:
float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4, from_block_size,
3*to_block_size].
"""
exp_blocked_to_pad = torch.cat(
[to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], dim=2
)
band_mask = torch.einsum("blq,blk->blqk", from_blocked_mask[:, 2:-2], exp_blocked_to_pad)
band_mask.unsqueeze_(1)
return band_mask | 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_length)
return blocked_encoder_mask, band_mask, from_mask, to_mask
def _pad_to_block_size(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor):
"""A helper function to pad tokens and mask to work with implementation of BigBird block-sparse attention."""
# padding
block_size = self.config.block_size
batch_size, seq_len = hidden_states.shape[:2] | 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}"
)
pad_id = self.config.pad_token_id
device = hidden_states.device
input_ids_padding = torch.ones((batch_size, padding_len), dtype=torch.long, device=device) * pad_id
inputs_embeds_padding = self.embed_tokens(input_ids_padding)
hidden_states = torch.cat([hidden_states, inputs_embeds_padding], dim=-2)
attention_mask = nn.functional.pad(
attention_mask, (0, padding_len), value=0
) # no attention on the padding tokens
return padding_len, hidden_states, attention_mask | 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, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.embed_tokens = BigBirdPegasusScaledWordEmbedding(
config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale
)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight | 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_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value | 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_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it. | 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*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**. | 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
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: | 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 performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
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, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. | 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)`.
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 into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*): | 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_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
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 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 = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
) | 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=input_shape[-1]
)
# embed positions
positions = self.embed_positions(input_shape, past_key_values_length)
positions = positions.to(inputs_embeds.device)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False | 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):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue | 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,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None), | 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,
)
hidden_states = layer_outputs[0] | 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],)
hidden_states = self.layernorm_embedding(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,) | 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 BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
) | 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
embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.shared = BigBirdPegasusScaledWordEmbedding(
vocab_size, config.d_model, padding_idx, embed_scale=embed_scale
)
self.encoder = BigBirdPegasusEncoder(config, self.shared)
self.decoder = BigBirdPegasusDecoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared | 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):
return self.decoder | 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.modeling_bart.BartModel.forward with Bart->BigBirdPegasus
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None, | 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 models, BigBirdPegasus automatically creates decoder_input_ids from
# input_ids if no decoder_input_ids are provided
if decoder_input_ids is None and decoder_inputs_embeds is None:
if input_ids is None:
raise ValueError(
"If no `decoder_input_ids` or `decoder_inputs_embeds` are "
"passed, `input_ids` cannot be `None`. Please pass either "
"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
) | 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_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 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_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
) | 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,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs | 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_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
) | 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, config: BigBirdPegasusConfig):
super().__init__(config)
self.model = BigBirdPegasusModel(config)
self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder() | 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 _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings | 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: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None, | 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 masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. | 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
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
) | 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_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0])
lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device) | 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:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
) | 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:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2])
+ layer_past[2:],
)
return reordered_past | 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(config)
self.classification_head = BigBirdPegasusClassificationHead(
config.d_model,
config.d_model,
config.num_labels,
config.classifier_dropout,
)
# Initialize weights and apply final processing
self.post_init() | 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.BartForSequenceClassification.forward
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None, | 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*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False | 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,
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_attn_head_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0] # last hidden state
eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device) | 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.classification_head(sentence_representation)
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.config.num_labels == 1:
self.config.problem_type = "regression"
elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification" | 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 == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output | 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,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_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.model = BigBirdPegasusModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init() | 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.BartForQuestionAnswering.forward
def forward(
self,
input_ids: torch.Tensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None, | 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"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss. | 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 end_positions is not None:
use_cache = False | 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_attn_head_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous() | 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:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2 | 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=start_logits,
end_logits=end_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,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
) | 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().__init__(config)
self.decoder = BigBirdPegasusDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs) | 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.model = BigBirdPegasusDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder | 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_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): | 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*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**. | 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 model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: | 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, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**. | 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, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
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. | 2,983 | /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)`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`). | 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.
output_hidden_states (`bool`, *optional*):
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.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, BigBirdPegasusForCausalLM | 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 configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
```"""
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 else self.config.use_return_dict | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
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