text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.v... | 3,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
value_states = torch.cat(torch.chunk(value_states, 2, dim=1), dim=-1)
value_states = value_states.repeat(1, 2, 1, 1)
causal_mask = attention_mask
if at... | 3,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
is_causal = True if ca... | 3,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
query_states.dtype
)
lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
query_states.dtype
)
lambda_full = lambda_1 - l... | 3,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: DiffLlamaConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.self_attn = DIFFLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) | 3,539 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaPreTrainedModel(LlamaPreTrainedModel):
_supports_flex_attn = False | 3,540 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaModel(LlamaModel):
pass | 3,541 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaForCausalLM(GemmaForCausalLM):
pass | 3,542 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaForSequenceClassification(LlamaForSequenceClassification):
pass | 3,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaForQuestionAnswering(LlamaForQuestionAnswering):
pass | 3,544 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaForTokenClassification(LlamaForTokenClassification):
pass | 3,545 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class SqueezeBertTokenizer(PreTrainedTokenizer):
r"""
Construct a SqueezeBERT tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic toke... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengt... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
Whether or not to tokenize Chinese characters. | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
stri... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
) | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
def _tokenize(self, text, split_special_tokens=False):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(
text, never_split=self.all_special_tokens if not split_special_tokens else None
):
# If the token is par... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls ... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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 ... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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. A SqueezeBERT sequence
pair mask has the following format... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
... | 3,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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(... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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
#... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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)) | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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 = []
... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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(" ")
... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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 ... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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... | 3,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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... | 3,548 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.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 = []
... | 3,548 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens | 3,548 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py |
class SqueezeBertEmbeddings(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.embedding_size, padding_idx=config.pad_token_id)
self.positi... | 3,549 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inpu... | 3,549 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings | 3,549 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class MatMulWrapper(nn.Module):
"""
Wrapper for torch.matmul(). This makes flop-counting easier to implement. Note that if you directly call
torch.matmul() in your code, the flop counter will typically ignore the flops of the matmul.
"""
def __init__(self):
super().__init__()
def forwa... | 3,550 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertLayerNorm(nn.LayerNorm):
"""
This is a nn.LayerNorm subclass that accepts NCW data layout and performs normalization in the C dimension.
N = batch C = channels W = sequence length
"""
def __init__(self, hidden_size, eps=1e-12):
nn.LayerNorm.__init__(self, normalized_shape=... | 3,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class ConvDropoutLayerNorm(nn.Module):
"""
ConvDropoutLayerNorm: Conv, Dropout, LayerNorm
"""
def __init__(self, cin, cout, groups, dropout_prob):
super().__init__()
self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups)
self.layernorm = Squee... | 3,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class ConvActivation(nn.Module):
"""
ConvActivation: Conv, Activation
"""
def __init__(self, cin, cout, groups, act):
super().__init__()
self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups)
self.act = ACT2FN[act]
def forward(self, x):
... | 3,553 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertSelfAttention(nn.Module):
def __init__(self, config, cin, q_groups=1, k_groups=1, v_groups=1):
"""
config = used for some things; ignored for others (work in progress...) cin = input channels = output channels
groups = number of groups to use in conv1d layers
"""
... | 3,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
self.query = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=q_groups)
self.key = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=k_groups)
self.value = nn.Conv1d(in_channels=cin, out_channels=cin, kernel_size=1, groups=v_groups)
self.dropout = nn.Dropout(c... | 3,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
def transpose_key_for_scores(self, x):
"""
- input: [N, C, W]
- output: [N, C1, C2, W] where C1 is the head index, and C2 is one head's contents
"""
new_x_shape = (x.size()[0], self.num_attention_heads, self.attention_head_size, x.size()[-1]) # [N, C1, C2, W]
x = x.view(... | 3,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
The attention_mask data layout is [N, W], and it does not need to be transposed.
"""
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
... | 3,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
# 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(attention_probs)
context_layer = self.matmul_qkv(attention_probs, value_layer)
context_layer = self.transpose_ou... | 3,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertModule(nn.Module):
def __init__(self, config):
"""
- hidden_size = input chans = output chans for Q, K, V (they are all the same ... for now) = output chans for
the module
- intermediate_size = output chans for intermediate layer
- groups = number of groups... | 3,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
self.attention = SqueezeBertSelfAttention(
config=config, cin=c0, q_groups=config.q_groups, k_groups=config.k_groups, v_groups=config.v_groups
)
self.post_attention = ConvDropoutLayerNorm(
cin=c0, cout=c1, groups=config.post_attention_groups, dropout_prob=config.hidden_dropout_pr... | 3,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
post_attention_output = self.post_attention(attention_output, hidden_states)
intermediate_output = self.intermediate(post_attention_output)
layer_output = self.output(intermediate_output, post_attention_output)
output_dict = {"feature_map": layer_output}
if output_attentions:
... | 3,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
assert config.embedding_size == config.hidden_size, (
"If you want embedding_size != intermediate hidden_size, "
"please insert a Conv1d layer to adjust the number of channels "
"... | 3,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
# [batch_size, sequence_length, hidden_size] --> [batch_size, hidden_size, sequence_length]
hidden_states = hidden_states.permute(0, 2, 1)
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for layer in self.layers:
i... | 3,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, a... | 3,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertPooler(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):
# We "pool" the model by simply taking the hidden state corresponding
... | 3,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertPredictionHeadTransform(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:
se... | 3,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = SqueezeBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder... | 3,559 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = SqueezeBertLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores | 3,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SqueezeBertConfig
base_model_prefix = "transformer" | 3,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv1d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.no... | 3,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertModel(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = SqueezeBertEmbeddings(config)
self.encoder = SqueezeBertEncoder(config)
self.pooler = SqueezeBertPooler(config)
# Initialize weights and apply final pr... | 3,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
@add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids... | 3,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 3,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
# 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]
... | 3,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
embedding_output = self.embeddings(
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
)
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_ma... | 3,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.transformer = SqueezeBertModel(config)
self.cls = SqueezeBertOnlyMLMHead(config)
... | 3,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
@add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[t... | 3,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.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]`
"""
... | 3,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
outputs = self.transformer(
input_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_attentions,
output_hi... | 3,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.transformer = SqueezeBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_p... | 3,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
@add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: ... | 3,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 3,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
outputs = self.transformer(
input_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_attentions,
output_hi... | 3,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
sel... | 3,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.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... | 3,564 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertForMultipleChoice(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = SqueezeBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initia... | 3,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
@add_start_docstrings_to_model_forward(
SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
... | 3,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
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.co... | 3,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
... | 3,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits... | 3,565 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertForTokenClassification(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = SqueezeBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = n... | 3,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
@add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Opt... | 3,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
outputs = self.transformer(
input_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_attentions,
output_hi... | 3,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,566 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertForQuestionAnswering(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = SqueezeBertModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initi... | 3,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
@add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_i... | 3,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
... | 3,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
outputs = self.transformer(
input_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_attentions,
output_hi... | 3,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 3,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 3,567 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py |
class SqueezeBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SqueezeBertModel`]. It is used to instantiate a
SqueezeBERT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | 3,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the SqueezeBERT model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`SqueezeBertModel`].
hidden_size (`int`, *optional*, defaults to 768):
... | 3,568 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py |
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