text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
return_dict: Optional[bool] = None,
) -> Union[Tuple, XLMProphetNetSeq2SeqModelOutput]:
r"""
Returns: | 10,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
Example:
```python
>>> from transformers import AutoTokenizer, XLMProphetNetModel
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = XLMProphetNetModel.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone... | 10,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
>>> last_hidden_states = outputs.last_hidden_state # main stream hidden states
>>> last_hidden_states_ngram = outputs.last_hidden_state_ngram # predict hidden states
```"""
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions i... | 10,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.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... | 10,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
# decoder outputs consists of (dec_features, past_key_values, 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,... | 10,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
if not return_dict:
return decoder_outputs + encoder_outputs
return XLMProphetNetSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
last_hidden_state_ngram=decoder_outputs.last_hidden_state_ngram,
past_key_values=decoder_outputs.past_key_valu... | 10,129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetForConditionalGeneration(XLMProphetNetPreTrainedModel):
_tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight", "lm_head.weight"]
def __init__(self, config: XLMProphetNetConfig):
super().__init__(config)
self.prophetnet = XLMProphetNetModel(c... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
@add_start_docstrings_to_model_forward(XLM_PROPHETNET_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=XLMProphetNetSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
d... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, XLMProphetNetSeq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices shoul... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, XLMProphetNetForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = XLMProphetNetForConditionalGeneration.from_pretraine... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
>>> logits_next_token = outputs.logits # logits to predict next token as usual
>>> logits_ngram_next_tokens = outputs.logits_ngram # logits to predict 2nd, 3rd, ... next tokens
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is no... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
outputs = self.prophetnet(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
predicting_streams = outputs[1].view(batch_size, self.config.ngram, sequence_length, -1)
predict_logits = self.lm_head(predicting_streams)
logits = predict_logits[:, 0]
logits_ngram = predict_logits[:, 1:] if self.config.ngram > 1 else None
# To use .view in loss computation, make sure... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
if not return_dict:
all_logits = tuple(v for v in [logits, logits_ngram] if v is not None)
return (loss,) + all_logits + outputs[2:] if loss is not None else all_logits + outputs[2:]
else:
return XLMProphetNetSeq2SeqLMOutput(
loss=loss,
logits=... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
def _compute_loss(self, logits, labels, ignore_index=-100):
expend_targets = labels.new_zeros(self.config.ngram, labels.size(0), labels.size(1)).fill_(ignore_index)
for i in range(self.config.ngram):
if i > 0 and self.disable_ngram_loss:
break
expend_targets[i, :... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
eps_i = self.config.eps / lprobs.size(-1)
loss = (1.0 - self.config.eps) * loss + eps_i * smooth_loss
return loss
def prepare_inputs_for_generation(
self,
decoder_input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
decoder_head_... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
if past_key_values:
decoder_input_ids = decoder_input_ids[:, -1:]
# first step, decoder_cached_states are empty
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.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... | 10,130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetForCausalLM(XLMProphetNetPreTrainedModel):
_tied_weights_keys = [
"prophetnet.word_embeddings.weight",
"prophetnet.decoder.word_embeddings.weight",
"lm_head.weight",
]
def __init__(self, config: XLMProphetNetConfig):
# set config for CLM
config = c... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.prophetnet.decoder.word_embeddings, self.lm_head)
... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
@add_start_docstrings_to_model_forward(XLM_PROPHETNET_STANDALONE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=XLMProphetNetDecoderLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
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 model is configured as a decoder.
encoder_attention_mask (`torch.Flo... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomp... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0,... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
>>> tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = XLMProphetNetForCausalLM.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
>>> tokenizer_enc = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")
>>> tokenizer_dec = AutoTokenizer.from_pretrained("patrickvonplaten/xprophetnet-large-uncased-standalone")
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained(
... "google-bert/bert-large-uncased"... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
>>> loss = outputs.loss
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
outputs = self.prophetnet.decoder(
input_ids=input_ids,
attent... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
predicting_streams = outputs[1].view(batch_size, self.config.ngram, sequence_length, -1)
predict_logits = self.lm_head(predicting_streams)
logits = predict_logits[:, 0]
logits_ngram = predict_logits[:, 1:] if self.config.ngram > 1 else None
loss = None
if labels is not None:
... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
if not return_dict:
all_logits = tuple(v for v in [logits, logits_ngram] if v is not None)
return (loss,) + all_logits + outputs[2:] if loss is not None else all_logits + outputs[2:]
else:
return XLMProphetNetDecoderLMOutput(
loss=loss,
logits=... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
for i in range(self.config.ngram):
if i > 0 and self.disable_ngram_loss:
break
expend_targets[i, :, :] = labels
logits = logits.transpose(0, 1).contiguous()
lprobs = nn.functional.log_softmax(
logits.view(-1, logits.size(-1)),
dim=-1,
... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
use_cache=None,
**kwargs,
):
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_p... | 10,131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetDecoderWrapper(XLMProphetNetPreTrainedModel):
"""
This is a wrapper class, so that [`XLMProphetNetForCausalLM`] can correctly be loaded from pretrained XLMProphetNet
classes.
"""
def __init__(self, config: XLMProphetNetConfig):
super().__init__(config)
self.word_... | 10,132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py |
class XLMProphetNetTokenizer(PreTrainedTokenizer):
"""
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
thi... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip> | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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 sequen... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is th... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming tha... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
def __init__(
self,
vocab_file,
bos_token="[SEP]",
eos_token="[SEP]",
sep_token="[SEP]",
unk_token="[UNK]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
)... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | ... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
# TODO ArthurZ fairseq_ids_to_tokens should be removed
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
unk_token=unk_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
def __setstate__(self, d):
self.__dict__ = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
" pip install sentence... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is ... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLMProphetNet
does not make use of token type ids, theref... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text: str) -> str:
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
return out_string
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = N... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.seri... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: list of [input IDs](../glossary#input-ids) with... | 10,133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py |
class XLMProphetNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`XLMProphetNetModel`]. It is used to instantiate a
XLMProphetNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults... | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
Args:
activation_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for activations inside the fully connected layer.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder... | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
Number of encoder layers.
num_encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the `intermediate` (often named feed-... | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight ma... | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first
token.
num_buckets (`int`, *optional*, defaults to 32)
The number of buckets to use for each attention layer. This is for relative position calculation. See the
[T5 pap... | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
smoothing is performed.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
""" | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
model_type = "xlm-prophetnet"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_attention_heads": "num_encoder_attention_heads",
} | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
def __init__(
self,
activation_dropout: Optional[float] = 0.1,
activation_function: Optional[Union[str, Callable]] = "gelu",
vocab_size: Optional[int] = 30522,
hidden_size: Optional[int] = 1024,
encoder_ffn_dim: Optional[int] = 4096,
num_encoder_layers: Optional[i... | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
disable_ngram_loss: Optional[bool] = False,
eps: Optional[float] = 0.0,
use_cache: Optional[bool] = True,
pad_token_id: Optional[int] = 0,
bos_token_id: Optional[int] = 1,
eos_token_id: Optional[int] = 2,
**kwargs,
):
self.vocab_size = vocab_size
self.... | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
# parameters for xlmprophetnet
self.ngram = ngram
self.num_buckets = num_buckets
self.relative_max_distance = relative_max_distance
self.disable_ngram_loss = disable_ngram_loss
self.eps = eps
# 3 Types of Dropout
self.attention_dropout = attention_dropout
... | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
@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 `num_encoder_layers` and"
" `num_decoder_layers`."
) | 10,134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py |
class QDQBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`QDQBertModel`]. It is used to instantiate an
QDQBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | 10,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the QDQBERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`QDQBertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Di... | 10,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.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... | 10,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py |
The epsilon used by the layer normalization layers.
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model shoul... | 10,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py |
Examples:
```python
>>> from transformers import QDQBertModel, QDQBertConfig
>>> # Initializing a QDQBERT google-bert/bert-base-uncased style configuration
>>> configuration = QDQBertConfig()
>>> # Initializing a model from the google-bert/bert-base-uncased style configuration
>>> model = QDQ... | 10,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py |
def __init__(
self,
vocab_size=30522,
hidden_size=768,
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_position_embeddings=512,
... | 10,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py |
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden... | 10,135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py |
class QDQBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embe... | 10,136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len ... | 10,136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values_length: int = 0,
) -> torch.Tensor:
... | 10,136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(... | 10,136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
... | 10,136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the num... | 10,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = confi... | 10,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_... | 10,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(sel... | 10,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
query_layer = self.transpose_for_scores(mixed_query_layer)
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 ... | 10,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(
self.matmul_q_input_quantizer(query_layer), self.matmul_k_input_quantizer(key_layer.transpose(-1, -2))
)
if self.position_embedding_type == "relative_key" or self.posit... | 10,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
... | 10,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attent... | 10,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs | 10,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
# Quantize Linear layer
self.dense = quant_nn.QuantLinear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn... | 10,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
# Quantize the inputs to the residual add
add_local = self.add_local_input_quantizer(hidden_states)
add_residual = self.add_residual_input_quantiz... | 10,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = QDQBertSelfAttention(config)
self.output = QDQBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads... | 10,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
... | 10,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
# Quantize Linear layer
self.dense = quant_nn.QuantLinear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hid... | 10,140 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
# Quantize Linear layer
self.dense = quant_nn.QuantLinear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = n... | 10,141 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
# Quantize the inputs to the residual add
add_local = self.add_local_input_quantizer(hidden_states)
add_residual = self.add_residual_input_quantiz... | 10,141 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_len_dim = 1
self.attention = QDQBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
... | 10,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is... | 10,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
... | 10,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
... | 10,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
layer_output = self.feed_forward_chunk(attention_output)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_o... | 10,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([QDQBertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | 10,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = p... | 10,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.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
layer_... | 10,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
output_attentions,
) | 10,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attent... | 10,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
... | 10,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hi... | 10,144 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.t... | 10,145 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.