text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Example:
```python
>>> from transformers import FalconModel, FalconConfig
>>> # Initializing a small (2-layer) Falcon configuration
>>> configuration = FalconConfig(num_hidden_layers=2)
>>> # Initializing a model from the small configuration
>>> model = FalconModel(configuration)
>>> # A... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
def __init__(
self,
vocab_size=65024,
hidden_size=4544,
num_hidden_layers=32,
num_attention_heads=71,
num_ln_in_parallel_attn=None,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
hidden_dropout=0.0,
attention_dropo... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
self.num_attention_heads = num_attention_heads
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.bos_token_id = bos_token_... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
self.ffn_hidden_size = hidden_size * 4
else:
self.ffn_hidden_size = ffn_hidden_size | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@property
def head_dim(self):
return self.hidden_size // self.num_attention_heads
@property
def rotary(self):
return not self.alibi | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
class BlenderbotSmallLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, input_ids_shape: torch.... | 9,364 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallAttention(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,
... | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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() | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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"
... | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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.
... | 9,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallEncoderLayer(nn.Module):
def __init__(self, config: BlenderbotSmallConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=c... | 9,366 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,366 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,366 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,366 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallDecoderLayer(nn.Module):
def __init__(self, config: BlenderbotSmallConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=c... | 9,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
config=conf... | 9,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallPreTrainedModel(PreTrainedModel):
config_class = BlenderbotSmallConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.nor... | 9,368 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallEncoder(BlenderbotSmallPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BlenderbotSmallEncoderLayer`].
Args:
config: BlenderbotSmallConfig
embed_tokens (nn.Embedding): output embedding
"""
... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([BlenderbotSmallEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(embed_dim)
... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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*):
... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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,
... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallDecoder(BlenderbotSmallPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BlenderbotSmallDecoderLayer`]
Args:
config: BlenderbotSmallConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(sel... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
self.embed_positions = BlenderbotSmallLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList([BlenderbotSmallDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_mo... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_attentions=... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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**,
... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of ... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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, ... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidd... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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):
... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
past_key_value = past_key_values[idx] if past_key_values is not None else None | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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,
... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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,
)
... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel):
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]
def __init__(self, config: BlenderbotSmallConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
s... | 9,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
@add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decod... | 9,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
r"""
Returns: | 9,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
Example:
```python
>>> from transformers import AutoTokenizer, BlenderbotSmallModel
>>> model = BlenderbotSmallModel.from_pretrained("facebook/blenderbot_small-90M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
>>> inputs = tokenizer("Studies ... | 9,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 3, 512]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_h... | 9,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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,
... | 9,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, co... | 9,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
@add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
at... | 9,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels ... | 9,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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
... | 9,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallDecoderWrapper(BlenderbotSmallPreTrainedModel):
"""
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().__... | 9,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel, 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.... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it. | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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*):
... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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,... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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, ... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
>>> model = BlenderbotSmallForCausalLM.from_pretrained("facebook/blenderbot_small-90M", add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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,
... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.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... | 9,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py |
class FlaxBlenderbotSmallAttention(nn.Module):
config: BlenderbotSmallConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.emb... | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
d... | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
... | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update... | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
"""Input shape: Batch x Time x Channel"... | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
# handle cache prepare causal attention mask
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
... | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask =... | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
... | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights | 9,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallEncoderLayer(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32 | 9,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBlenderbotSmallAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.encoder_attention_heads,
dropout=self.config.attention_dropout,
dty... | 9,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | 9,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states,... | 9,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs | 9,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
class FlaxBlenderbotSmallEncoderLayerCollection(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBlenderbotSmallEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in rang... | 9,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py |
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