text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
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... | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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() | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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"
... | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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.
... | 3,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianEncoderLayer(nn.Module):
def __init__(self, config: MarianConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_head... | 3,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_stat... | 3,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
residual = 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... | 3,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianDecoderLayer(nn.Module):
def __init__(self, config: MarianConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_head... | 3,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
config=config,
... | 3,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatT... | 3,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
# Fully Connected
residual = 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.dro... | 3,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianPreTrainedModel(PreTrainedModel):
config_class = MarianConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module: Union[nn.Linear, nn.Embedding, MarianSinusoidalPositionalEmbedding]):
std = self.config.init_std
if isinstance(mod... | 3,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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,
"decoder_i... | 3,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianEncoder(MarianPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`MarianEncoderLayer`].
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: Maria... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
self.embed_positions = MarianSinusoidalPositionalEmbedding(
config.max_position_embeddings, embed_dim, self.padding_idx
)
self.layers = nn.ModuleList([MarianEncoderLayer(config) for _ in range(config.encoder_layers)])
self.gradient_checkpointing = False
# Initialize weights ... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: O... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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**,
... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if out... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layers)
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
for idx,... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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,
... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
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(
last_hidden_state=hidden_states, hidden_states=encoder_st... | 3,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianDecoder(MarianPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MarianDecoderLayer`]
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MarianConfig, embed_token... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
self.embed_positions = MarianSinusoidalPositionalEmbedding(
config.max_position_embeddings, config.d_model, self.padding_idx
)
self.layers = nn.ModuleList([MarianDecoderLayer(config) for _ in range(config.decoder_layers)])
self.gradient_checkpointing = False
# Initialize wei... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
provide it. | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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*):
... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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
... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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. | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=in... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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 | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
assert attn_mask.size()[0] == (len(self.layers)), (
... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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,
... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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,
)
... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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],)
# a... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianModel(MarianPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: MarianConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
# We always use self.shared... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
self.encoder = MarianEncoder(config, encoder_embed_tokens)
self.decoder = MarianDecoder(config, decoder_embed_tokens)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
# This will return shared embeddings if they are shared else specif... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
def get_decoder_input_embeddings(self):
if self.config.share_encoder_decoder_embeddings:
raise ValueError(
"`get_decoder_input_embeddings` should not be called if `config.share_encoder_decoder_embeddings` "
"is `True`. Please use `get_input_embeddings` instead."
... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
def get_decoder(self):
return self.decoder
def resize_decoder_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
if self.config.share_encoder_decoder_embeddings:
raise ValueError(
"`resize_decoder_token_embeddings` should not be called if `config.share_encoder_... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
@add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Option... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
return_dict: Optional[bool] = None,
) -> Seq2SeqModelOutput:
r"""
Returns: | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
Example:
```python
>>> from transformers import AutoTokenizer, MarianModel
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = MarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> inputs = tokenizer("Studies have been shown that own... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 26, 512]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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,
... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianMTModel(MarianPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
"final_logits_bias",
"encoder.embed_positions.weight",
"decoder.embed_positions.weight",
]
_keys_to_ignore_on_save = ["model.encoder.embed_positions.weight... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
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_token... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
new_num_tokens = new_embeddings.weight.shape[0]
# update config.decoder_vocab_size if embeddings are tied
if self.config.share_encoder_decoder_embeddings:
self.config.decoder_vocab_size = new_num_tokens
# if word embeddings are not tied, make sure that lm head is resized as well
... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
def resize_decoder_token_embeddings(self, new_num_tokens):
if self.config.share_encoder_decoder_embeddings:
raise ValueError(
"`resize_decoder_token_embeddings` should not be called if `config.share_encoder_decoder_embeddings` "
"is `True`. Please use `resize_token_em... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
if new_num_tokens is None:
return model_embeds
# Update base model and current model config
self.config.decoder_vocab_size = new_num_tokens
# Tie weights again if needed
self.tie_weights()
self._resize_final_logits_bias(new_num_tokens)
return model_embeds
... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
def tie_weights(self):
"""
Tie the weights between the input embeddings and the output embeddings.
If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the
weights instead.
"""
output_embeddings = self.get_output_embeddi... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
if getattr(self.config, "is_encoder_decoder", False) and getattr(self.config, "tie_encoder_decoder", False):
if hasattr(self, self.base_model_prefix):
self = getattr(self, self.base_model_prefix)
tied_weights = self._tie_encoder_decoder_weights(
self.encoder, self... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
@add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(MARIAN_GENERATION_EXAMPLE)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.T... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Seq2SeqLMOutput:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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
... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,734 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianDecoderWrapper(MarianPreTrainedModel):
"""
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)
... | 3,735 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianForCausalLM(MarianPreTrainedModel, 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 = MarianDeco... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_att... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it. | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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*):
... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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,... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.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, ... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
label... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
ou... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
>>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en", add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("H... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs... | 3,736 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py |
class MarianConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an
Marian model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | 3,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
Args:
vocab_size (`int`, *optional*, defaults to 58101):
Vocabulary size of the Marian model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MarianModel`] or [`TFMarianModel`].
d_model (`int`, *optional*, defaults to 1024)... | 3,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"... | 3,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard d... | 3,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models)
forced_eos_token_id (`int`, *optional*, defaults to 0):
The id of the token to force as the last generated token when `max_length` is reached... | 3,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
Examples:
```python
>>> from transformers import MarianModel, MarianConfig
>>> # Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration
>>> configuration = MarianConfig()
>>> # Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration
>>> model = MarianModel(c... | 3,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
def __init__(
self,
vocab_size=58101,
decoder_vocab_size=None,
max_position_embeddings=1024,
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
e... | 3,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropo... | 3,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
**kwargs,
) | 3,737 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
class MarianOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = OrderedDict(
[
... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
elif self.task == "causal-lm":
# TODO: figure this case out.
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
... | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
]
) | 3,738 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py |
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