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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... | 3,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100EncoderLayer(nn.Module):
def __init__(self, config: M2M100Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = M2M100_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_head... | 3,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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)
... | 3,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100DecoderLayer(nn.Module):
def __init__(self, config: M2M100Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = M2M100_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_head... | 3,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = M2M100_ATTENTION_CLASSES[config._attn_implementation](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
config=config,
... | 3,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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) | 3,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100PreTrainedModel(PreTrainedModel):
config_class = M2M100Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["M2M100EncoderLayer", "M2M100DecoderLayer"]
_supports_flash_attn_2 = True
_supports_sdpa = True
def _init_weights(self, module):
... | 3,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100Encoder(M2M100PreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`M2M100EncoderLayer`].
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: M2M10... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
self.embed_positions = M2M100SinusoidalPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
self.padding_idx,
)
self.layers = nn.ModuleList([M2M100EncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_m... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Opti... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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**,
... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
# expand attention_mask
if attention_mask is not None:
if self._use_flash_attention_2:
attention_mask = attention_mask if 0 in attention_mask else None
elif self._use_sdpa and head_mask is None and not output_attentions:
# output_attentions=True & head_mas... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
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 No... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100Decoder(M2M100PreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`M2M100DecoderLayer`]
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: M2M100Config, embed_token... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
self.embed_positions = M2M100SinusoidalPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
self.padding_idx,
)
self.layers = nn.ModuleList([M2M100DecoderLayer(config) for _ in range(config.decoder_layers)])
self._use_flash_attention_2 = config... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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*):
... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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
... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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. | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
if self._use_flash_attention_2:
# 2d mask is passed through the layers
combined_attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
elif self._use_sdpa and not output_attentions and cross_attn_head_mask is None:
# output_attention... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
if self._use_flash_attention_2:
encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
elif self._use_sdpa and cross_attn_head_ma... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
) | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
# embed positions
positions = self.embed_positions(input_ids, inputs_embeds, 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)
... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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):
... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
skip_the_layer = True if self.training and (dropout_probability < self.layerdrop) else False
if not skip_the_layer or synced_gpus:
# under fsdp or deepspeed zero3 all gpus must run in sync
past_key_value = past_key_values[idx] if past_key_values is not None else None | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
combined_attention_mask,
encoder_hidden_states,
... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
hidden_states = layer_outputs[0]
if skip_the_layer:
continue
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
all_cross_at... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100Model(M2M100PreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: M2M100Config):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embed_scale = math.sqrt(conf... | 3,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
... | 3,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
@add_start_docstrings_to_model_forward(M2M_100_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
atten... | 3,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
ou... | 3,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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,
... | 3,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100ForConditionalGeneration(M2M100PreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: M2M100Config):
super().__init__(config)
self.model = M2M... | 3,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
@add_start_docstrings_to_model_forward(M2M_100_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(M2M_100_GENERATION_EXAMPLE)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Opti... | 3,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for c... | 3,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if decoder_input_ids is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_s... | 3,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.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... | 3,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
masked_lm_loss = None
if labels is not None:
# move labels to the correct device to enable PP
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 ... | 3,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_p... | 3,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py |
class M2M100Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an
M2M100 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | 3,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the M2M100 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`M2M100Model`] or
d_model (`int`, *optional*, defaults to 1024):
Dimen... | 3,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.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"... | 3,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
The dropout ratio for classifier.
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*, def... | 3,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
Whether or not the model should return the last key/values attentions (not used by all models). | 3,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
Example:
```python
>>> from transformers import M2M100Config, M2M100Model
>>> # Initializing a M2M100 facebook/m2m100_418M style configuration
>>> configuration = M2M100Config()
>>> # Initializing a model (with random weights) from the facebook/m2m100_418M style configuration
>>> model = M2M1... | 3,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
def __init__(
self,
vocab_size=128112,
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,
encoder_layerdrop=0.05,
d... | 3,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = act... | 3,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
**kwargs,
) | 3,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
class M2M100OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
... | 3,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
# Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering
# A better name would be _generate_dummy_inputs_for_encoder_and_decoder because sequence classification and question
# answering are not supported for M2M100, but this name is preserved to be able to check tha... | 3,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
batch_size = compute_effective_axis_dimension(
batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
) | 3,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
seq_length = compute_effective_axis_dimension(
seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=... | 3,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._generate_dummy_inputs_for_default_and_seq2seq_lm
def _generate_dummy_inputs_for_default_and_seq2seq_lm(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = ... | 3,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
# Generate decoder inputs
decoder_seq_length = seq_length if not self.use_past else 1
decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
tokenizer, batch_size, decoder_seq_length, is_pair, framework
)
decoder_inputs = {f"decoder_{... | 3,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, encoder_seq_length = common_inputs["input_ids"].shape
decoder_seq_length = common... | 3,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
common_inputs["decoder_attention_mask"] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
)
common_inputs["past_key_values"] = []
# If the number of encoder and decoder layers are present in the model configuration,... | 3,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
for _ in range(min_num_layers):
common_inputs["past_key_values"].append(
(
torch.zeros(decoder_shape),
torch.zeros(decoder_shape),
torch.zeros(encoder_shape),
torch.zeros(encoder_shape),
... | 3,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py |
class M2M100Tokenizer(PreTrainedTokenizer):
"""
Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information rega... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
Args:
vocab_file (`str`):
Path to the vocabulary file.
spm_file (`str`):
Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary.
src_lang (`str`, *optional*):
A string rep... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
language_codes (`... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming tha... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
>>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
>>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro")
>>> src_text = " UN Chief Says There Is No Military Solution in Syria"
>>> tgt_text = "Şeful ONU declară că nu există o sol... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
def __init__(
self,
vocab_file,
spm_file,
src_lang=None,
tgt_lang=None,
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
pad_token="<pad>",
unk_token="<unk>",
language_codes="m2m100",
sp_model_kwargs: Optional[Dict[str, ... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
additional_special_tokens = kwargs.pop("additional_special_tokens", [])
for lang_code in fairseq_language_code:
token = self.get_lang_token(lang_code)
if token not in additional_special_tokens and lang_code not in str(token) not in self.added_tokens_encoder:
additional_sp... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
self._src_lang = src_lang if src_lang is not None else "en"
self.tgt_lang = tgt_lang
self.cur_lang_id = self.get_lang_id(self._src_lang)
self.num_madeup_words = num_madeup_words
super().__init__(
src_lang=src_lang,
tgt_lang=tgt_lang,
bos_token=bos_to... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
def get_vocab(self) -> Dict:
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
@property
def src_lang(self) -> str:
return self._src_lang
@src_lang.setter
def src_lang(self, new_src_lang: str)... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is ... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
prefix_ones = [1] * len(self.prefix_tokens)
suffix_ones = [1] * len(self.suffix_tokens)
if token_ids_1 is None:
return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
def build_inputs_wit... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) wit... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
save_dir = Path(save_directory)
if not save_dir.is_dir():
raise OSError(f"{save_directory} should be a directory")
v... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file):
copyfile(self.spm_file, spm_save_path)
elif not os.path.isfile(self.spm_file):
with open(spm_save_path, "wb") as fi:
content_spiece_model = self.sp_model.serialized_mode... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
"""Used by translation pipeline, to prepare inputs for the generate function"""
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and ... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
def set_src_lang_special_tokens(self, src_lang: str) -> None:
"""Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
lang_token = self.get_lang_token(src_lang)
self.cur_lang_id = self.lang_token_to_id[lang_token]
self.prefix_tokens = [self.c... | 3,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py |
class Olmo2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Olmo2RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | 3,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
class Olmo2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Olmo2Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head... | 3,446 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
... | 3,446 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
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