text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Args:
langs (`List[str]`, *optional*):
A list of two languages to translate from and to, for instance `["en", "ru"]`.
src_vocab_file (`str`, *optional*):
File containing the vocabulary for the source language.
tgt_vocab_file (`st`, *optional*):
File containing... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.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>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from m... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
def __init__(
self,
langs=None,
src_vocab_file=None,
tgt_vocab_file=None,
merges_file=None,
do_lower_case=False,
unk_token="<unk>",
bos_token="<s>",
sep_token="</s>",
pad_token="<pad>",
**kwargs,
):
try:
impo... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
if langs and len(langs) == 2:
self.src_lang, self.tgt_lang = langs
else:
raise ValueError(
f"arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. "
"Usually that means that tokenizer can't find a mapping for the given model path "... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
with open(src_vocab_file, encoding="utf-8") as src_vocab_handle:
self.encoder = json.load(src_vocab_handle)
with open(tgt_vocab_file, encoding="utf-8") as tgt_vocab_handle:
tgt_vocab = json.load(tgt_vocab_handle)
self.decoder = {v: k for k, v in tgt_vocab.items()}
wit... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
# hack override
def get_vocab(self) -> Dict[str, int]:
return self.get_src_vocab()
# hack override
@property
def vocab_size(self) -> int:
return self.src_vocab_size
def moses_punct_norm(self, text, lang):
if lang not in self.cache_moses_punct_normalizer:
punct_n... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
def moses_detokenize(self, tokens, lang):
if lang not in self.cache_moses_detokenizer:
moses_detokenizer = self.sm.MosesDetokenizer(lang=lang)
self.cache_moses_detokenizer[lang] = moses_detokenizer
return self.cache_moses_detokenizer[lang].detokenize(tokens)
def moses_pipeli... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
def bpe(self, token):
word = tuple(token[:-1]) + (token[-1] + "</w>",)
if token in self.cache:
return self.cache[token]
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_rank... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.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
... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
Args:
- lang: ISO language code (default = 'en') (string). Languages should belong of the model supported
languages. However, we don't enforce it.
- bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False)
(bool). If True, we... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
split_tokens = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(token).split(" ")))
return split_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.enc... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.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 FAIRSEQ Transformer... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.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 ... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
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=True
)
# no bos used in fairseq
if token_ids_1 is not None:
return ([0] * len(token_ids_0)) + [1] + ([0]... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
src_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["src_vocab_file"]
)
tgt_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["tgt_vocab_file"]
... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
index = 0
with open(merges_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 {merges_file}: BPE merge... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.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 | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
class XmodEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(confi... | 9,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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 ... | 9,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
... | 9,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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(... | 9,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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)
... | 9,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
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}) i... | 9,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":... | 9,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 9,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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... | 9,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
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 ... | 9,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = q... | 9,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("b... | 9,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in XmodModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scor... | 9,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
i... | 9,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodSelfOutput(nn.Module):
# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput.__init__
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=confi... | 9,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = XmodSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = XmodSelfOutput(config)
self.pruned_heads = set()
self.pre_norm = c... | 9,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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) | 9,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 9,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs | 9,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.interm... | 9,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodAdapter(nn.Module):
def __init__(self, config):
super().__init__()
self.bottleneck_size = config.hidden_size // config.adapter_reduction_factor
self.dense1 = nn.Linear(config.hidden_size, self.bottleneck_size)
self.dense2 = nn.Linear(self.bottleneck_size, config.hidden_size... | 9,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.ln_before_adapter = config.ln_before_adapter
self... | 9,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, lang_ids: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
hidden_states = self.lang_adapter(lang_id... | 9,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
split_hidden_states = torch.split(hidden_states, lang_lengths.tolist(), 0)
lang_wise_outputs = []
for i, (lang_id, split_hidden_state) in enumerate(zip(lang_ids, split_hidden_states)):
lang = list(self.adapter_modules.keys())[int(lang_id.item())]
lang_wise_outputs.append(self.ada... | 9,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = XmodAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.a... | 9,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
def forward(
self,
hidden_states: torch.Tensor,
lang_ids: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[t... | 9,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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
... | 9,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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,
... | 9,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
residual = attention_output
if self.pre_norm:
attention_output = self.output.LayerNorm(attention_output)
intermediate_output = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output,... | 9,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([XmodLayer(config) for _ in range(config.num_hidden_layers)])
self.is_pre_norm = config.pre_norm
if self.is_pre_norm:
self.LayerNorm = nn.L... | 9,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
def forward(
self,
hidden_states: torch.Tensor,
lang_ids: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[t... | 9,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None | 9,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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... | 9,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
lang_ids,
attention_mask,
layer_head_mask,
en... | 9,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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... | 9,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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,
]
... | 9,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodPooler(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 hidde... | 9,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XmodConfig
base_model_prefix = "roberta"
supports_gradient_checkpointing = True | 9,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
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 ... | 9,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
def set_default_language(self, language: str):
"""
Set the default language code for the model. This is used when the language is not specified in the input.
Args:
language (`str`): The language code, such as `"en_XX"` or `"de_DE"`.
"""
if language not in self.config... | 9,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
def freeze_embeddings_and_language_adapters(self):
"""
Freeze the embeddings and language adapters of the model. Usually, this is applied before the model is
fine-tuned on a downstream task.
"""
logger.info("Freezing embeddings")
for parameter in self.roberta.embeddings.p... | 9,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodModel(XmodPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in *Attention is
all you need*_ by Ashish Vaswani, Noam... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
# Copied from transformers.models.clap.modeling_clap.ClapTextModel.__init__ with ClapText->Xmod
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = XmodEmbeddings(config)
self.encoder = XmodEncoder(config)
self... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
# Copied from transformers.models.roberta.modeling_roberta.RobertaModel._prune_heads
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, ... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
@add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
lang_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optiona... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **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 pre... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
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... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
if lang_ids is None:
if self.config.default_language is None:
raise ValueError("Input language unknown. Please call `XmodPreTrainedModel.set_default_language()`")
adapter_languages = list(self.encoder.layer[0].output.adapter_modules.keys())
default_lang_id = adapter_l... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
token_type_ids = buffer... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embeddi... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hid... | 9,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodForCausalLM(XmodPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
... | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
@add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
lang_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids... | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
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
... | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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,... | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`... | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")
>>> config = AutoConfig.from_pretrained("facebook/xmod-base")
>>> config.is_decoder = True
>>> model = XmodForCausalLM.from_pretrained("facebook/xmod-base", config=config)
>>> model.set_default_language("en_XX")... | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
outputs = self.roberta(
input_ids,
lang_ids=lang_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_h... | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
... | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM._reorder_cache
def _reorder_cache(self, 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_s... | 9,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodForMaskedLM(XmodPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
# Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
if config.is_de... | 9,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
# Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings | 9,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
@add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
lang_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids... | 9,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
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]`
kwarg... | 9,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
outputs = self.roberta(
input_ids,
lang_ids=lang_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_h... | 9,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
... | 9,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodLMHead(nn.Module):
"""Roberta Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder... | 9,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
def _tie_weights(self):
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
# For accelerate compatibility and to not break backward compatibility
if self.decoder.bias.device.type == "meta":
self.decoder.bias = self.bias
else:
... | 9,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodForSequenceClassification(XmodPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
... | 9,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
@add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
lang_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids... | 9,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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 | 9,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
outputs = self.roberta(
input_ids,
lang_ids=lang_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attent... | 9,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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... | 9,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodForMultipleChoice(XmodPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForMultipleChoice.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
self.roberta = XmodModel(config)
self.dropout = nn.Dropout(config.hidden... | 9,539 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
@add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
lang_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
at... | 9,539 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.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... | 9,539 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_lang_ids = lang_ids.repeat(input_ids.size(0) * input_ids.size(1)) if lang_ids is not None else None
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
... | 9,539 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.