text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "funnel", None) is not None:
with tf.name_scope(self.funnel.name):
self.funnel.build(None)
if getattr(self, "qa_outputs", None) is not None:
wit... | 9,464 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py |
class FunnelConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FunnelModel`] or a [`TFBertModel`]. It is used to
instantiate a Funnel Transformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with ... | 9,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Funnel transformer. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`FunnelModel`] or [`TFFunnelModel`].
block_sizes (`List[int]`, *optional*, d... | 9,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py |
Dimensionality of the model's heads.
d_inner (`int`, *optional*, defaults to 3072):
Inner dimension in the feed-forward blocks.
hidden_act (`str` or `callable`, *optional*, defaults to `"gelu_new"`):
The non-linear activation function (function or string) in the encoder and poole... | 9,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py |
The upper bound of the *uniform initializer* for initializing all weight matrices in attention layers.
initializer_std (`float`, *optional*):
The standard deviation of the *normal initializer* for initializing the embedding matrix and the weight of
linear layers. Will default to 1 for th... | 9,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py |
Whether or not to separate the cls token when applying pooling.
truncate_seq (`bool`, *optional*, defaults to `True`):
When using `separate_cls`, whether or not to truncate the last token when pooling, to avoid getting a
sequence length that is not a multiple of 2.
pool_q_only (`... | 9,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py |
model_type = "funnel"
attribute_map = {
"hidden_size": "d_model",
"num_attention_heads": "n_head",
} | 9,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py |
def __init__(
self,
vocab_size=30522,
block_sizes=[4, 4, 4],
block_repeats=None,
num_decoder_layers=2,
d_model=768,
n_head=12,
d_head=64,
d_inner=3072,
hidden_act="gelu_new",
hidden_dropout=0.1,
attention_dropout=0.1,
... | 9,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py |
self.n_head = n_head
self.d_head = d_head
self.d_inner = d_inner
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.initializer_range = initializer_range
... | 9,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py |
super().__init__(**kwargs)
@property
def num_hidden_layers(self):
return sum(self.block_sizes)
@num_hidden_layers.setter
def num_hidden_layers(self, value):
raise NotImplementedError(
"This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.... | 9,465 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py |
class FunnelEmbeddings(nn.Module):
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
... | 9,466 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelAttentionStructure(nn.Module):
"""
Contains helpers for `FunnelRelMultiheadAttention `.
"""
cls_token_type_id: int = 2
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.config = config
self.sin_dropout = nn.Dropout(config.hidden_dropout)
... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def init_attention_inputs(
self,
inputs_embeds: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor]:
"""Returns the attention inputs associated to the inputs of the model."""
# inputs_emb... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def token_type_ids_to_mat(self, token_type_ids: torch.Tensor) -> torch.Tensor:
"""Convert `token_type_ids` to `token_type_mat`."""
token_type_mat = token_type_ids[:, :, None] == token_type_ids[:, None]
# Treat <cls> as in the same segment as both A & B
cls_ids = token_type_ids == self.cl... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
For the relative shift attention, it returns all possible vectors R used in the paper, appendix A.2.1, final
formula. | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
Paper link: https://arxiv.org/abs/2006.03236
"""
d_model = self.config.d_model
if self.config.attention_type == "factorized":
# Notations from the paper, appending A.2.2, final formula.
# We need to create and return the matrices phi, psi, pi and omega.
pos_se... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
pi = torch.cat([cos_embed_d, cos_embed_d], dim=-1)
omega = torch.cat([-sin_embed, cos_embed], dim=-1)
return (phi, pi, psi, omega)
else:
# Notations from the paper, appending A.2.1, final formula.
# We need to create and return all the possible vectors R for all b... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
pos = torch.arange(0, seq_len, dtype=torch.int64, device=device).to(dtype)
pooled_pos = pos
position_embeds_list = []
for block_index in range(0, self.config.num_blocks):
# For each block with block_index > 0, we need two types position embeddings:
# ... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
# construct rel_pos_id
stride = 2 ** (block_index - 1)
rel_pos = self.relative_pos(pos, stride, pooled_pos, shift=2)
rel_pos = rel_pos[:, None] + zero_offset
rel_pos = rel_pos.expand(rel_pos.size(0), d_model)
position_em... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def stride_pool_pos(self, pos_id: torch.Tensor, block_index: int):
"""
Pool `pos_id` while keeping the cls token separate (if `config.separate_cls=True`).
"""
if self.config.separate_cls:
# Under separate <cls>, we treat the <cls> as the first token in
# the previ... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def relative_pos(self, pos: torch.Tensor, stride: int, pooled_pos=None, shift: int = 1) -> torch.Tensor:
"""
Build the relative positional vector between `pos` and `pooled_pos`.
"""
if pooled_pos is None:
pooled_pos = pos
ref_point = pooled_pos[0] - pos[0]
nu... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
# Do the stride pool recursively if axis is a list or a tuple of ints.
if isinstance(axis, (list, tuple)):
for ax in axis:
tensor = self.stride_pool(tensor, ax)
return tensor
# Do the stride pool recursively if tensor is a list or tuple of tensors.
if isi... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def pool_tensor(
self, tensor: Union[torch.Tensor, Tuple[torch.Tensor], List[torch.Tensor]], mode: str = "mean", stride: int = 2
) -> torch.Tensor:
"""Apply 1D pooling to a tensor of size [B x T (x H)]."""
if tensor is None:
return None
# Do the pool recursively if tenso... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
if mode == "mean":
tensor = nn.functional.avg_pool2d(tensor, stride, stride=stride, ceil_mode=True)
elif mode == "max":
tensor = nn.functional.max_pool2d(tensor, stride, stride=stride, ceil_mode=True)
elif mode == "min":
tensor = -nn.functional.max_pool2d(-tensor, str... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def pre_attention_pooling(
self, output, attention_inputs: Tuple[torch.Tensor]
) -> Tuple[torch.Tensor, Tuple[torch.Tensor]]:
"""Pool `output` and the proper parts of `attention_inputs` before the attention layer."""
position_embeds, token_type_mat, attention_mask, cls_mask = attention_input... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
attention_mask = self.pool_tensor(attention_mask, mode="min")
output = self.pool_tensor(output, mode=self.config.pooling_type)
attention_inputs = (position_embeds, token_type_mat, attention_mask, cls_mask)
return output, attention_inputs | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def post_attention_pooling(self, attention_inputs: Tuple[torch.Tensor]) -> Tuple[torch.Tensor]:
"""Pool the proper parts of `attention_inputs` after the attention layer."""
position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
if self.config.pool_q_only:
self.p... | 9,467 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelRelMultiheadAttention(nn.Module):
def __init__(self, config: FunnelConfig, block_index: int) -> None:
super().__init__()
self.config = config
self.block_index = block_index
d_model, n_head, d_head = config.d_model, config.n_head, config.d_head
self.hidden_dropout... | 9,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
self.post_proj = nn.Linear(n_head * d_head, d_model)
self.layer_norm = nn.LayerNorm(d_model, eps=config.layer_norm_eps)
self.scale = 1.0 / (d_head**0.5)
def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None):
"""Relative attention score for the position... | 9,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
# Shape batch_size x sea_len x n_head x d_model
q_r_attention = torch.einsum("binh,dnh->bind", q_head + u, w_r)
q_r_attention_1 = q_r_attention * phi[:, None]
q_r_attention_2 = q_r_attention * pi[:, None]
# Shape batch_size x n_head x seq_len x context_len
po... | 9,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
# Shape max_rel_len x n_head x d_model
r_head = torch.einsum("td,dnh->tnh", r, w_r)
# Shape batch_size x n_head x seq_len x max_rel_len
positional_attn = torch.einsum("binh,tnh->bnit", q_head + v, r_head)
# Shape batch_size x n_head x seq_len x context_len
pos... | 9,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
# Shape batch_size x n_head x seq_len x 2
token_type_bias = torch.einsum("bind,snd->bnis", q_head + r_s_bias, self.seg_embed)
# Shape batch_size x n_head x seq_len x context_len
token_type_mat = token_type_mat[:, None].expand([batch_size, q_head.shape[2], seq_len, context_len])
# Shapes ... | 9,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_inputs: Tuple[torch.Tensor],
output_attentions: bool = False,
) -> Tuple[torch.Tensor, ...]:
# query has shape batch_size x seq_len x d_model
# key and value have... | 9,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
q_head = q_head * self.scale
# Shape n_head x d_head
r_w_bias = self.r_w_bias * self.scale
# Shapes batch_size x n_head x seq_len x context_len
content_score = torch.einsum("bind,bjnd->bnij", q_head + r_w_bias, k_head)
positional_attn = self.relative_positional_attention(position... | 9,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
# attention output, shape batch_size x seq_len x n_head x d_head
attn_vec = torch.einsum("bnij,bjnd->bind", attn_prob, v_head)
# Shape shape batch_size x seq_len x d_model
attn_out = self.post_proj(attn_vec.reshape(batch_size, seq_len, n_head * d_head))
attn_out = self.hidden_dropout(at... | 9,468 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelPositionwiseFFN(nn.Module):
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.linear_1 = nn.Linear(config.d_model, config.d_inner)
self.activation_function = ACT2FN[config.hidden_act]
self.activation_dropout = nn.Dropout(config.activation_dropout)
... | 9,469 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelLayer(nn.Module):
def __init__(self, config: FunnelConfig, block_index: int) -> None:
super().__init__()
self.attention = FunnelRelMultiheadAttention(config, block_index)
self.ffn = FunnelPositionwiseFFN(config)
def forward(
self,
query: torch.Tensor,
... | 9,470 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelEncoder(nn.Module):
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.config = config
self.attention_structure = FunnelAttentionStructure(config)
self.blocks = nn.ModuleList(
[
nn.ModuleList([FunnelLayer(config, block_in... | 9,471 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def forward(
self,
inputs_embeds: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[Tuple, BaseM... | 9,471 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
for block_index, block in enumerate(self.blocks):
pooling_flag = hidden.size(1) > (2 if self.config.separate_cls else 1)
pooling_flag = pooling_flag and block_index > 0
if pooling_flag:
pooled_hidden, attention_inputs = self.attention_structure.pre_attention_pooling(
... | 9,471 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
hidden = layer_output[0]
if do_pooling:
attention_inputs = self.attention_structure.post_attention_pooling(attention_inputs) | 9,471 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
if output_attentions:
all_attentions = all_attentions + layer_output[1:]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden,)
if not return_dict:
return tuple(v for v in [hidden, all_hidden_states, all_attenti... | 9,471 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelDecoder(nn.Module):
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.config = config
self.attention_structure = FunnelAttentionStructure(config)
self.layers = nn.ModuleList([FunnelLayer(config, 0) for _ in range(config.num_decoder_layers)])
d... | 9,472 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
hidden = upsampled_hidden + first_block_hidden
all_hidden_states = (hidden,) if output_hidden_states else None
all_attentions = () if output_attentions else None
attention_inputs = self.attention_structure.init_attention_inputs(
hidden,
attention_mask=attention_mask,
... | 9,472 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelDiscriminatorPredictions(nn.Module):
"""Prediction module for the discriminator, made up of two dense layers."""
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.config = config
self.dense = nn.Linear(config.d_model, config.d_model)
self.dens... | 9,473 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FunnelConfig
load_tf_weights = load_tf_weights_in_funnel
base_model_prefix = "funnel" | 9,474 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
def _init_weights(self, module):
classname = module.__class__.__name__
if classname.find("Linear") != -1:
if getattr(module, "weight", None) is not None:
if self.config.initializer_std is None:
fan_out, fan_in = module.weight.shape
std ... | 9,474 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
nn.init.uniform_(module.seg_embed, b=self.config.initializer_range)
elif classname == "FunnelEmbeddings":
std = 1.0 if self.config.initializer_std is None else self.config.initializer_std
nn.init.normal_(module.word_embeddings.weight, std=std)
if module.word_embeddings.paddin... | 9,474 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelClassificationHead(nn.Module):
def __init__(self, config: FunnelConfig, n_labels: int) -> None:
super().__init__()
self.linear_hidden = nn.Linear(config.d_model, config.d_model)
self.dropout = nn.Dropout(config.hidden_dropout)
self.linear_out = nn.Linear(config.d_model, n... | 9,475 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelForPreTrainingOutput(ModelOutput):
"""
Output type of [`FunnelForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss of the ELECTRA-style objective.
logits (`torch.FloatTensor` of shape `(batch_s... | 9,476 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 9,476 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelBaseModel(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.embeddings = FunnelEmbeddings(config)
self.encoder = FunnelEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def... | 9,477 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small-base",
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Op... | 9,477 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,477 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 9,477 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
encoder_outputs = self.encoder(
inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return encoder_... | 9,477 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelModel(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.config = config
self.embeddings = FunnelEmbeddings(config)
self.encoder = FunnelEncoder(config)
self.decoder = FunnelDecoder(config)
# Initialize... | 9,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch... | 9,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 9,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
encoder_outputs = self.encoder(
inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict,
)
decoder_outputs = self.decoder(... | 9,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
if not return_dict:
idx = 0
outputs = (decoder_outputs[0],)
if output_hidden_states:
idx += 1
outputs = outputs + (encoder_outputs[1] + decoder_outputs[idx],)
if output_attentions:
idx += 1
outputs = outputs ... | 9,478 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelForPreTraining(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.funnel = FunnelModel(config)
self.discriminator_predictions = FunnelDiscriminatorPredictions(config)
# Initialize weights and apply final processing
... | 9,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=FunnelForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[tor... | 9,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
- 0 indicates the token is an original token,
- 1 indicates the token was replaced.
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, FunnelForPreTraining
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("funnel-transfo... | 9,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
discriminator_hidden_states = self.funnel(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict... | 9,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
loss = None
if labels is not None:
loss_fct = nn.BCEWithLogitsLoss()
if attention_mask is not None:
active_loss = attention_mask.view(-1, discriminator_sequence_output.shape[1]) == 1
active_logits = logits.view(-1, discriminator_sequence_output.shape[1])[a... | 9,479 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelForMaskedLM(FunnelPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.funnel = FunnelModel(config)
self.lm_head = nn.Linear(config.d_model, config.vocab_size)
# Initialize weights... | 9,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<mask>",
)
def forward(
self,
inp... | 9,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
outputs = self.funnel(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
... | 9,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,480 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelForSequenceClassification(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.funnel = FunnelBaseModel(config)
self.classifier = FunnelClassificationHead(... | 9,481 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small-base",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
inpu... | 9,481 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,481 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
outputs = self.funnel(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
... | 9,481 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 9,481 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelForMultipleChoice(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.funnel = FunnelBaseModel(config)
self.classifier = FunnelClassificationHead(config, 1)
# Initialize weights and apply final processing
self.p... | 9,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint="funnel-transformer/small-base",
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self... | 9,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds... | 9,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
... | 9,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
last_hidden_state = outputs[0]
pooled_output = last_hidden_state[:, 0]
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits,... | 9,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelForTokenClassification(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.funnel = FunnelModel(config)
self.dropout = nn.Dropout(config.hidden_dropout)
self.classifier = nn.L... | 9,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional... | 9,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
outputs = self.funnel(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
... | 9,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class FunnelForQuestionAnswering(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.funnel = FunnelModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Ini... | 9,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: O... | 9,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.Lo... | 9,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
outputs = self.funnel(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
... | 9,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeze(-1)
if len(end_positions.size()) > 1:
e... | 9,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 9,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py |
class PhiAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper""" | 9,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config... | 9,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
self.qk_layernorm = config.qk_layernorm
if self.qk_layernorm:
self.q_layernorm = nn.LayerNorm(
config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True
)
self.k_layernorm = nn.LayerNorm(
config.hidden... | 9,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torc... | 9,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
cos, sin = position_embeddings
# Partial rotary embedding
query_rot, query_pass = (
query_states[..., : self.rotary_ndims],
query_states[..., self.rotary_ndims :],
)
key_rot, key_pass = (
key_states[..., : self.rotary_ndims],
key_states[...... | 9,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 9,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
... | 9,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
class PhiMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
... | 9,486 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py |
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