text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
class SudachiTokenizer:
"""Runs basic tokenization with Sudachi morphological parser."""
def __init__(
self,
do_lower_case=False,
never_split=None,
normalize_text=True,
trim_whitespace=False,
sudachi_split_mode="A",
sudachi_config_path=None,
sudac... | 9,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
Args:
**do_lower_case**: (*optional*) boolean (default True)
Whether to lowercase the input.
**never_split**: (*optional*) list of str
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedToken... | 9,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
dict type of sudachi, choose from `["small", "core", "full"]`.
**sudachi_projection**: (*optional*) string
Word projection mode of sudachi, choose from `["surface", "normalized", "reading", "dictionary", "dictionary_and_surface", "normalized_and_surface", "normalized_nouns"]`.
""" | 9,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
self.do_lower_case = do_lower_case
self.never_split = never_split if never_split is not None else []
self.normalize_text = normalize_text
self.trim_whitespace = trim_whitespace
try:
from sudachipy import dictionary, tokenizer
except ImportError:
raise Imp... | 9,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
sudachi_dictionary = dictionary.Dictionary(
config_path=sudachi_config_path, resource_dir=sudachi_resource_dir, dict=sudachi_dict_type
)
if is_sudachi_projection_available():
self.sudachi = sudachi_dictionary.create(self.split_mode, projection=self.projection)
elif self.p... | 9,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
if self.do_lower_case and token not in never_split:
token = token.lower()
if self.trim_whitespace:
if token.strip() == "":
continue
else:
token = token.strip()
tokens.append(token)
return tokens | 9,505 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
class JumanppTokenizer:
"""Runs basic tokenization with jumanpp morphological parser."""
def __init__(
self,
do_lower_case=False,
never_split=None,
normalize_text=True,
trim_whitespace=False,
):
"""
Constructs a JumanppTokenizer.
Args:
... | 9,506 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
self.do_lower_case = do_lower_case
self.never_split = never_split if never_split is not None else []
self.normalize_text = normalize_text
self.trim_whitespace = trim_whitespace
try:
import rhoknp
except ImportError:
raise ImportError(
"You... | 9,506 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
if self.do_lower_case and token not in never_split:
token = token.lower()
if self.trim_whitespace:
if token.strip() == "":
continue
else:
token = token.strip()
tokens.append(token)
return tokens | 9,506 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
class CharacterTokenizer:
"""Runs Character tokenization."""
def __init__(self, vocab, unk_token, normalize_text=True):
"""
Constructs a CharacterTokenizer.
Args:
**vocab**:
Vocabulary object.
**unk_token**: str
A special symbol f... | 9,507 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
Returns:
A list of characters.
"""
if self.normalize_text:
text = unicodedata.normalize("NFKC", text)
output_tokens = []
for char in text:
if char not in self.vocab:
output_tokens.append(self.unk_token)
continue
... | 9,507 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two s... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
#... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split)) | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is ... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(cha... | 9,508 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | 9,509 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
... | 9,509 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens | 9,509 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
class SentencepieceTokenizer:
"""
Runs sentencepiece tokenization. Based on transformers.models.albert.tokenization_albert.AlbertTokenizer.
"""
def __init__(
self,
vocab,
unk_token,
do_lower_case=False,
remove_space=True,
keep_accents=True,
sp_mod... | 9,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
if not self.keep_accents:
outputs = unicodedata.normalize("NFKD", outputs)
outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def tokenize(self, text):
"""
Token... | 9,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
Returns:
A list of sentencepiece tokens.
"""
text = self.preprocess_text(text)
pieces = self.sp_model.encode(text, out_type=str)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
cur_p... | 9,510 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py |
class DecoderConfig(PretrainedConfig):
r"""
Configuration class for FSMT's decoder specific things. note: this is a private helper class
"""
model_type = "fsmt_decoder"
def __init__(self, vocab_size=0, bos_token_id=0):
super().__init__()
self.vocab_size = vocab_size
self.bo... | 9,511 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
class FSMTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FSMTModel`]. It is used to instantiate a FSMT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
Args:
langs (`List[str]`):
A list with source language and target_language (e.g., ['en', 'ru']).
src_vocab_size (`int`):
Vocabulary size of the encoder. Defines the number of different tokens that can be represented by the
`inputs_ids` passed to the forward method in ... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimens... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
max_position_embeddings (`int`, *optional*, ... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
eos_token_id (`int`, *optional*, defaults to 2)
End of stream token id.
decoder_start_token_id (`int`, *optional*):
This model starts decoding with `eos_token_id`
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
Google "layerdrop arxiv", as its not explainabl... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
Exponential penalty to the length that is used with beam-based generation. It is applied as an exponent to
the sequence length, which in turn is used to divide the score of the sequence. Since the score is the log
likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
`eos_token_id`. | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
Examples:
```python
>>> from transformers import FSMTConfig, FSMTModel
>>> # Initializing a FSMT facebook/wmt19-en-ru style configuration
>>> config = FSMTConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = FSMTModel(config)
>>> # Accessing the mo... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
# update the defaults from config file
def __init__(
self,
langs=["en", "de"],
src_vocab_size=42024,
tgt_vocab_size=42024,
activation_function="relu",
d_model=1024,
max_length=200,
max_position_embeddings=1024,
encoder_ffn_dim=4096,
enc... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
self.langs = langs
self.src_vocab_size = src_vocab_size
self.tgt_vocab_size = tgt_vocab_size
self.d_model = d_model # encoder_embed_dim and decoder_embed_dim | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = self.num_hidden_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.decoder_ffn_dim = decoder_ffn_dim... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
# 3 Types of Dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.dropout = dropout
self.use_cache = use_cache
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id... | 9,512 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py |
class PretrainedFSMTModel(PreTrainedModel):
config_class = FSMTConfig
base_model_prefix = "model"
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
... | 9,513 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
class EncoderLayer(nn.Module):
def __init__(self, config: FSMTConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Attention(self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout)
self.self_attn_layer_norm = LayerNorm(self.embed_dim)... | 9,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def forward(self, x, encoder_padding_mask, layer_head_mask, output_attentions=False):
"""
Args:
x (`torch.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
encoder_padding_mask (`torch.ByteTensor`): binary ByteTensor of shape
*(batch, src_len)* w... | 9,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
Returns:
encoded output of shape *(seq_len, batch, embed_dim)*
"""
residual = x
x, attn_weights = self.self_attn(
query=x,
key=x,
key_padding_mask=encoder_padding_mask,
layer_head_mask=layer_head_mask,
output_attentions=outp... | 9,514 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
class FSMTEncoder(nn.Module):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`EncoderLayer`].
Args:
config: FSMTConfig
"""
def __init__(self, config: FSMTConfig, embed_tokens):
super().__init__()
self.dropout = config.d... | 9,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: torch.Tensor = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool ... | 9,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
Returns:
BaseModelOutput or Tuple comprised of:
- **x** (`torch.Tensor`): the last encoder layer's output of shape *(src_len, batch, embed_dim)*
- **encoder_states** (`Tuple(torch.FloatTensor)`): all intermediate hidden states of shape *(src_len,
batch, emb... | 9,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_... | 9,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
# B x T x C -> T x B x C
x = x.transpose(0, 1) | 9,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layers)
), ... | 9,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
x,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
) | 9,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
if output_attentions:
all_attentions = all_attentions + (attn,)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if output_hidden_states:
encoder_states += (x,)
if not return_dict:
return tuple(v for v in [x, encoder_states, all_attentions] if v i... | 9,515 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
class DecoderLayer(nn.Module):
def __init__(self, config: FSMTConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Attention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
... | 9,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def forward(
self,
x,
encoder_hidden_states,
encoder_attn_mask=None,
layer_state=None,
causal_mask=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
decoder_padding_mask=None,
output_attentions=False,
):
residual = x
... | 9,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
# Cross attention
residual = x
assert self.encoder_attn.cache_key != self.self_attn.cache_key
x, cross_attn_weights = self.encoder_attn(
query=x,
key=encoder_hidden_states,
key_padding_mask=encoder_attn_mask,
layer_state=layer_state, # mutates lay... | 9,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
# Fully Connected
residual = x
x = self.activation_fn(self.fc1(x))
x = nn.functional.dropout(x, p=self.activation_dropout, training=self.training)
x = self.fc2(x)
x = nn.functional.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.final_... | 9,516 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
class FSMTDecoder(nn.Module):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DecoderLayer`]
Args:
config: FSMTConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: FSMTConfig, embed_tokens: nn.Embedding):
... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
with deepspeed.zero.GatheredParameters(self.embed_tokens.weight, modifier_rank=None):
embed_tokens_weight_shape = self.embed_tokens.weight.shape
else:
embed_tokens_weight_shape = self.embed_tokens.weight.shape
self.output_projection = nn.Linear(embed_tokens_weight_shape[1], e... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def forward(
self,
input_ids: torch.Tensor,
encoder_hidden_states: torch.Tensor,
encoder_padding_mask: torch.Tensor,
decoder_padding_mask: torch.Tensor,
decoder_causal_mask: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[to... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
Args:
input_ids (`torch.LongTensor` of shape `(batch, tgt_len)`):
previous decoder outputs for teacher forcing
encoder_hidden_states: output from the encoder, used for
encoder-side attention
encoder_padding_mask: for ignoring pad tokens
pas... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
Returns:
BaseModelOutputWithPast or tuple:
- the decoder's features of shape *(batch, tgt_len, embed_dim)*
- the cache
- hidden states
- a... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
# embed positions
positions = self.embed_positions(input_ids)
if use_cache:... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
x += positions
x = nn.functional.dropout(x, p=self.dropout, training=self.training)
# Convert to FSMT output format: (BS, seq_len, model_dim) -> (seq_len, BS, model_dim)
x = x.transpose(0, 1)
encoder_hidden_states = encoder_hidden_states.transpose(0, 1)
# decoder layers
... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
# check if head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
assert attn_mask.size()[0] == (len(self.layers)), (
f"The ... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
layer_state = past_key_values[idx] if past_key_values is not None else None
x, layer_self_attn, layer_past, layer_cross_attn = decoder_layer(
x,
encoder_hidden_states,
encoder_attn_mask=encoder_padding_mask,
decoder_padding_mask=decoder_paddin... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
# add hidden states from the last decoder layer
if output_hidden_states:
x = x.transpose(0, 1)
all_hidden_states += (x,)
x = x.transpose(0, 1)
# Convert to standard output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
x = x.transpose(0, 1)
... | 9,517 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
class Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim,
num_heads,
dropout=0.0,
bias=True,
encoder_decoder_attention=False, # otherwise self_attention
):
super().__init__()
... | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
self.encoder_decoder_attention = encoder_decoder_attention
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)... | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def forward(
self,
query,
key: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
layer_state: Optional[Dict[str, Optional[Tensor]]] = None,
attn_mask: Optional[Tensor] = None,
layer_head_mask: Optional[Tensor] = None,
output_attentions=False,
... | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
else:
saved_state = None
layer_state = {} | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
q = self.q_proj(query) * self.scaling
if static_kv:
if key is None:
k = v = None
else:
k = self.k_proj(key)
v = self.v_proj(key)
else:
k = self.k_proj(query)
v = self.v_proj(query)
q = self._shape(q,... | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
assert k is not None
src_len = k.size(1)
attn_weights = torch.bmm(q, k.transpose(1, 2))
assert attn_weights.size() == (bsz * self.num_heads, tgt_len, src_len)
if attn_mask is not None:
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_mask
... | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
if key_padding_mask is not None: # don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
reshaped = key_padding_mask.unsqueeze(1).unsqueeze(2)
attn_weights = attn_weights.masked_fill(reshaped, torch.finfo(attn_weights.dtype).min)
... | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
if output_attentions:
# make sure that attn_weights are included in graph
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped... | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def _use_saved_state(self, k, v, saved_state, key_padding_mask, static_kv, bsz):
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if "prev_key" in saved_state:
_prev_key = saved_state["prev_key"]
assert _prev_key is not None
prev_key = _prev_ke... | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
prev_key_padding_mask: Optional[Tensor] = saved_state.get("prev_key_padding_mask", None)
if prev_key_padding_mask is not None:
if static_kv:
new_key_padding_mask = prev_key_padding_mask
else:
new_key_padding_mask = torch.cat([prev_key_padding_mask, key_pad... | 9,518 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
class FSMTModel(PretrainedFSMTModel):
_tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
def __init__(self, config: FSMTConfig):
super().__init__(config)
padding_idx = config.pad_token_id
encoder_embed_tokens = nn.Embedding(config.src_vocab_size, c... | 9,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.decoder.embed_tokens, self.get_input_embeddings())
self._tie_or_clone_weights(self.decoder.output_projection, self.get_input_embeddings()) | 9,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
@add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Seq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[... | 9,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
if decoder_input_ids is None:
use_cache = False | 9,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self... | 9,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
if decoder_input_ids is None and decoder_inputs_embeds is None:
raise ValueError("Make sure that `decoder_input_ids` or `decoder_inputs_embeds` are passed.") | 9,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden... | 9,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
decoder_input_ids,
encoder_outputs[0],
attention_mask,
decoder_padding_mask,
decoder_causal_mask=causal_mask,
inputs_embeds=deco... | 9,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_output... | 9,519 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
class FSMTForConditionalGeneration(PretrainedFSMTModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
def __init__(self, config: FSMTConfig):
super().__init__(config)
base_model = FSMTModel(config)
... | 9,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
@add_start_docstrings_to_model_forward(FSMT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(FSMT_GENERATION_EXAMPLE)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[t... | 9,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for c... | 9,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
outputs = self.model(
input_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
... | 9,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# TODO(SS): do we need to ignore pad tokens in labels?
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.tgt_vocab_size), labels.view(-1))
if not return_dict:
output = (lm_l... | 9,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id)
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = []
for layer_past in past_key_values:
# get the correct batch idx ... | 9,520 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
class SinusoidalPositionalEmbedding(nn.Embedding):
"""
This module produces sinusoidal positional embeddings of any length.
We don't want to save the weight of this embedding since it's not trained (deterministic) and it can be huge.
Padding symbols are ignored.
These embeddings get automatically... | 9,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def make_weight(self, num_positions, embedding_dim, padding_idx):
weight = self.get_embedding(num_positions, embedding_dim, padding_idx)
if not hasattr(self, "weight"):
# in ___init__
super().__init__(num_positions, embedding_dim, padding_idx, _weight=weight)
else:
... | 9,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
"Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * ... | 9,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
Position numbers begin at padding_idx+1. Padding symbols are ignored.
"""
# The series of casts and type-conversions here are carefully
# balanced to both work with ONNX export and XLA. In particular XLA
# prefers ints, cumsum defaults to output longs, and ONNX doesn't know
# how... | 9,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
def forward(
self,
input,
incremental_state: Optional[Any] = None,
timestep: Optional[Tensor] = None,
):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = input.shape[:2]
max_pos = self.padding_idx + 1 + seq_len
if max_pos > self.wei... | 9,521 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py |
class FSMTTokenizer(PreTrainedTokenizer):
"""
Construct an FAIRSEQ Transformer tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following:
- Moses preprocessing and tokenization.
- Normalizing all inputs text.
- The arguments `special_tokens` and the function `set_special_tok... | 9,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py |
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