text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
# 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,
... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.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... | 2,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py |
class BioGptTokenizer(PreTrainedTokenizer):
"""
Construct an FAIRSEQ Transformer tokenizer. Moses tokenization followed by Byte-Pair Encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regardi... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When bu... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequenc... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
def __init__(
self,
vocab_file,
merges_file,
unk_token="<unk>",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
pad_token="<pad>",
**kwargs,
):
try:
import sacremoses
except ImportError:
raise Import... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
""" Initialisation"""
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n"... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
def moses_tokenize(self, text, lang):
if lang not in self.cache_moses_tokenizer:
moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
self.cache_moses_tokenizer[lang] = moses_tokenizer
return self.cache_moses_tokenizer[lang].tokenize(
text, aggressive_dash_splits=True,... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decod... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BioGPT sequence has... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens ... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=Tru... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`Li... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (file... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge i... | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
try:
import sacremoses
except ImportError:
raise ImportError(
"You need to install sacremoses to use XLMTokenizer. "
"See https://pypi.org/project/sacremoses/ for installation."
)
self.sm = sacremoses | 2,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/tokenization_biogpt.py |
class BioGptLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# BioGpt is set up so that if padding_idx is specified then offset the embedding ids by 2
# and... | 2,985 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
return super().forward(positions + self.offset) | 2,985 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding... | 2,986 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptAttention(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,
c... | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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() | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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"
... | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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.
... | 2,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptSdpaAttention(BioGptAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[... | 2,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states,
key... | 2,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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() | 2,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
# get query proj
query_states = self.q_proj(hidden_states)
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
... | 2,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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... | 2,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptDecoderLayer(nn.Module):
def __init__(self, config: BioGptConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = BIOGPT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.num_attention_head... | 2,989 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool... | 2,989 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are r... | 2,989 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
hidden_states = self.self_attn_layer_norm(hidden_states)
# 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... | 2,989 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.... | 2,989 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BioGptConfig
base_model_prefix = "biogpt"
supports_gradient_checkpointing = True
_supports_sdpa =... | 2,990 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.... | 2,990 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptModel(BioGptPreTrainedModel):
def __init__(self, config: BioGptConfig):
super().__init__(config)
self.config = config
self.layerdrop = config.layerdrop
self.dropout = config.hidden_dropout_prob
self.embed_dim = config.hidden_size
self.padding_idx = config.... | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
self.gradient_checkpointing = False
self._use_sdpa = config._attn_implementation == "sdpa"
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens ... | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
@add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
... | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input = input_ids
input_shape = input.size()
eli... | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
if attention_mask is None:
attention_mask = torch.ones(
(inputs_embeds.shape[0], inputs_embeds.shape[1] + past_key_values_length),
dtype=torch.bool,
device=inputs_embeds.device,
)
elif attention_mask.shape[1] != past_key_values_length + inp... | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
if self._use_sdpa and not output_attentions and head_mask is None:
# output_attentions=True & head_mask can not be supported when using SDPA, fall back to
# the manual implementation that requires a 4D causal mask in all cases.
attention_mask = _prepare_4d_causal_attention_mask_for_s... | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.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
all_hidden_states = () if output_... | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
... | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
if output_attentions:
all_self_attns += (layer_outputs[1],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
hidden_states = self.layer_norm(hidden_states)
next_cache = next_decoder_cache if use_... | 2,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptForCausalLM(BioGptPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["output_projection.weight"]
def __init__(self, config):
super().__init__(config)
self.biogpt = BioGptModel(config)
self.output_projection = nn.Linear(config.hidden_size, config.vocab_size, bias=False... | 2,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
@add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_i... | 2,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_siz... | 2,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
outputs = self.biogpt(
input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_stat... | 2,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
if not return_dict:
output = (prediction_scores,) + outputs[1:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
... | 2,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptForTokenClassification(BioGptPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.biogpt = BioGptModel(config)
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
class... | 2,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
@add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
tok... | 2,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
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).
"""
... | 2,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
transformer_outputs = self.biogpt(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output... | 2,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
act... | 2,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptForSequenceClassification(BioGptPreTrainedModel):
def __init__(self, config: BioGptConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.biogpt = BioGptModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# In... | 2,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
@add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 2,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
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).
"""
... | 2,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
transformer_outputs = self.biogpt(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output... | 2,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
if self.config.pad_token_id is None:
sequence_length = -1
else:
if input_ids is not None:
sequence_length = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
else:
sequence_length = -1
logger.warning_... | 2,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
sel... | 2,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_... | 2,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def get_input_embeddings(s... | 2,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/modeling_biogpt.py |
class BioGptConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BioGptModel`]. It is used to instantiate an
BioGPT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | 2,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/configuration_biogpt.py |
Args:
vocab_size (`int`, *optional*, defaults to 42384):
Vocabulary size of the BioGPT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`BioGptModel`].
hidden_size (`int`, *optional*, defaults to 1024):
Dim... | 2,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/configuration_biogpt.py |
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The d... | 2,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/configuration_biogpt.py |
Scale embeddings by diving by sqrt(d_model).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
layerdrop (`float`, *optional*, defaults to ... | 2,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/configuration_biogpt.py |
Example:
```python
>>> from transformers import BioGptModel, BioGptConfig
>>> # Initializing a BioGPT microsoft/biogpt style configuration
>>> configuration = BioGptConfig()
>>> # Initializing a model from the microsoft/biogpt style configuration
>>> model = BioGptModel(configuration)
>>... | 2,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/configuration_biogpt.py |
def __init__(
self,
vocab_size=42384,
hidden_size=1024,
num_hidden_layers=24,
num_attention_heads=16,
intermediate_size=4096,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=1024,
... | 2,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/configuration_biogpt.py |
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.scale_embedding = scale_embedding
self.use_cache = use_cache
self.layerdrop = layerdrop
self.activation_dropout = activati... | 2,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/configuration_biogpt.py |
class Dictionary:
"""A mapping from symbols to consecutive integers"""
def __init__(
self,
*, # begin keyword-only arguments
bos="<s>",
pad="<pad>",
eos="</s>",
unk="<unk>",
extra_special_symbols=None,
):
self.bos_word, self.unk_word, self.pa... | 2,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/convert_biogpt_original_pytorch_checkpoint_to_pytorch.py |
def __len__(self):
"""Returns the number of symbols in the dictionary"""
return len(self.symbols)
def __contains__(self, sym):
return sym in self.indices
@classmethod
def load(cls, f):
"""Loads the dictionary from a text file with the format:
```
<symbol0> ... | 2,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/convert_biogpt_original_pytorch_checkpoint_to_pytorch.py |
def add_from_file(self, f):
"""
Loads a pre-existing dictionary from a text file and adds its symbols to this instance.
"""
if isinstance(f, str):
try:
with open(f, "r", encoding="utf-8") as fd:
self.add_from_file(fd)
except Fil... | 2,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/convert_biogpt_original_pytorch_checkpoint_to_pytorch.py |
for line in lines[indices_start_line:]:
try:
line, field = line.rstrip().rsplit(" ", 1)
if field == "#fairseq:overwrite":
overwrite = True
line, field = line.rsplit(" ", 1)
else:
overwrite = False
... | 2,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/convert_biogpt_original_pytorch_checkpoint_to_pytorch.py |
raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'") | 2,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/biogpt/convert_biogpt_original_pytorch_checkpoint_to_pytorch.py |
class VisualBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VisualBertModel`]. It is used to instantiate an
VisualBERT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yi... | 2,997 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/configuration_visual_bert.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the VisualBERT model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`VisualBertModel`]. Vocabulary size of the model. Defines the
different to... | 2,997 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/configuration_visual_bert.py |
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder ... | 2,997 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/configuration_visual_bert.py |
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`VisualBertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matri... | 2,997 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/configuration_visual_bert.py |
Whether or not the visual token type and position type embedding weights should be initialized the same as
the textual token type and positive type embeddings. When set to `True`, the weights of the textual token
type and position type embeddings are copied to the respective visual embedding lay... | 2,997 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/configuration_visual_bert.py |
Example:
```python
>>> from transformers import VisualBertConfig, VisualBertModel
>>> # Initializing a VisualBERT visualbert-vqa-coco-pre style configuration
>>> configuration = VisualBertConfig.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
>>> # Initializing a model (with random weights) fr... | 2,997 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/configuration_visual_bert.py |
def __init__(
self,
vocab_size=30522,
hidden_size=768,
visual_embedding_dim=512,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max... | 2,997 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/configuration_visual_bert.py |
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.visual_embedding_dim = visual_embedding_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_... | 2,997 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/configuration_visual_bert.py |
class VisualBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings and visual embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
... | 2,998 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
# For Visual Features
# Token type and position embedding for imag... | 2,998 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
self.visual_projection = nn.Linear(config.visual_embedding_dim, config.hidden_size)
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
visual_embeds=None,
visual_token_type_ids=None,
image_text_alignment=Non... | 2,998 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
# Absolute Position Embeddings
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
if visual_embeds is not None:
if visual_token_type_ids is None:
visual_token_type_ids = torch.ones(
visual_embeds.size()[... | 2,998 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
dtype = token_type_embeddings.dtype
image_text_alignment_mask = (image_text_alignment != -1).long()
# Get rid of the -1.
image_text_alignment = image_text_alignment_mask * image_text_alignment
# Batch x image_length x alignment length x dim
... | 2,998 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py |
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