text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,) | 10,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py |
class MBartLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
# and a... | 10,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding_... | 10,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
co... | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int... | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size() | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support... | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
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... | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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"
... | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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.
... | 10,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartFlashAttention2(MBartAttention):
"""
MBart flash attention module. This module inherits from `MBartAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with pa... | 10,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-... | 10,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions:... | 10,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# get query proj
query_states = self._reshape(self.q_proj(hidden_states), -1, bsz)
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` t... | 10,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
key_states = self._reshape(self.k_proj(hidden_states), -1, bsz)
value_states = self._reshape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0].transpose(1, 2), key_states], dim=1)
value_states = torch.cat([past_key_value[1].transpose(1, 2), value_states],... | 10,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference ... | 10,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_s... | 10,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
return attn_output, attn_weights, past_key_value | 10,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartSdpaAttention(MBartAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[to... | 10,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states,
key... | 10,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size() | 10,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# get query proj
query_states = self.q_proj(hidden_states)
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
... | 10,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
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... | 10,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
# The tgt_len > 1 is n... | 10,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask,
# but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
attn_output = torch.nn.functional.scaled_dot_prod... | 10,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# 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 a... | 10,072 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartEncoderLayer(nn.Module):
def __init__(self, config: MBartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MBART_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
... | 10,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq... | 10,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, traini... | 10,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
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)
... | 10,073 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartDecoderLayer(nn.Module):
def __init__(self, config: MBartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MBART_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | 10,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = MBART_ATTENTION_CLASSES[config._attn_implementation](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
config=config,
... | 10,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_l... | 10,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
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_a... | 10,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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) | 10,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,074 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartClassificationHead(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_di... | 10,075 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartPreTrainedModel(PreTrainedModel):
config_class = MBartConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["MBartDecoderLayer", "MBartAttention"]
_supports_flash_attn_2 = True
_supports_sdpa = True
def _init_weights(self, module):
... | 10,076 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,076 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartEncoder(MBartPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`MBartEncoderLayer`].
Args:
config: MBartConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MBartConf... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
self.embed_positions = MBartLearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([MBartEncoderLayer(config) for _ in range(config.encoder_layers)])
self.config = config
self.layernorm_embedding = nn.LayerNorm(embed_d... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optio... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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**,
... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input = input_ids
input_shape = input.shape
... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# expand attention_mask
if attention_mask is not None:
if self.config._attn_implementation == "flash_attention_2":
attention_mask = attention_mask if 0 in attention_mask else None
elif self.config._attn_implementation == "sdpa" and head_mask is None and not output_attenti... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# 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... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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,
... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
l... | 10,077 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartDecoder(MBartPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MBartDecoderLayer`]
Args:
config: MBartConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MBartConfig, embed_tokens: Op... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
self.embed_positions = MBartLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList([MBartDecoderLayer(config) for _ in range(config.decoder_layers)])
self.config = config
self.layernorm_embedding = nn.LayerNorm(c... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cr... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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*):
... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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
... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid perfo... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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. | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input = input_ids
input_shape = input.si... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
if self.config._attn_implementation == "flash_attention_2":
# 2d mask is passed through the layers
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
elif self.config._attn_implementation == "sdpa" and not output_attentions and cross_attn_he... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
if self.config._attn_implementation == "flash_attention_2":
encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
elif self.conf... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
) | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# embed positions
positions = self.embed_positions(input, past_key_values_length)
hidden_states = inputs_embeds + positions.to(inputs_embeds.device)
hidden_states = self.layernorm_embedding(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=sel... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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):
... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
past_key_value = past_key_values[idx] if past_key_values is not None else None | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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,
... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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,
)
... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartModel(MBartPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: MBartConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embed_scale = math.sqrt(config.... | 10,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder.embed_tokens, self.get_input_embeddings())
self._tie_or_clone_weights(self.decoder.embed_tokens, self.get_input_embeddings()) | 10,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
@add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_ids: to... | 10,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Seq2SeqModelOutput, Tuple[torch.FloatTensor]]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
... | 10,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
# different to other models, MBart 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:
decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id) | 10,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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,
... | 10,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartForConditionalGeneration(MBartPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
_tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: MB... | 10,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
@add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(MBART_GENERATION_EXAMPLE)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Ten... | 10,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Seq2SeqLMOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels ... | 10,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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
... | 10,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
d... | 10,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
# cached cross_attention states don't have to be reordered -> they are always the same
reordered_past += (
tuple(past_state.index_select(0, beam... | 10,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
class MBartForSequenceClassification(MBartPreTrainedModel):
_tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight"]
def __init__(self, config: MBartConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = MBartModel(config)
self.classifi... | 10,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
@add_start_docstrings_to_model_forward(MBART_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.... | 10,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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 ==... | 10,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.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... | 10,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py |
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