text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
class BlenderbotSmallTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" BlenderbotSmall tokenizer (backed by HuggingFace's *tokenizers* library).
Args:
vocab_file (`str`):
Path to the vocabulary file.
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_clas... | class_definition | 1,129 | 3,321 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small_fast.py | null | 9,400 |
class BlenderbotSmallTokenizer(PreTrainedTokenizer):
"""
Constructs a Blenderbot-90M tokenizer based on BPE (Byte-Pair-Encoding)
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
the superclass for more information regarding methods.
... | class_definition | 1,393 | 7,922 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py | null | 9,401 |
class MegatronBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position... | class_definition | 4,817 | 7,420 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,402 |
class MegatronBertSelfAttention(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_... | class_definition | 7,518 | 14,876 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,403 |
class MegatronBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Te... | class_definition | 14,990 | 15,457 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,404 |
class MegatronBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.self = MegatronBertSelfAttention(config)
self.output = MegatronBertSelfOutput(config)
self.pruned_heads = set()
... | class_definition | 15,539 | 17,664 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,405 |
class MegatronBertIntermediate(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:
sel... | class_definition | 17,761 | 18,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,406 |
class MegatronBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> to... | class_definition | 18,443 | 18,920 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,407 |
class MegatronBertLayer(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 = MegatronBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_atte... | class_definition | 19,001 | 23,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,408 |
class MegatronBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([MegatronBertLayer(config) for _ in range(config.num_hidden_layers)])
# The final layer norm. We removed the 1st LN, moved LN to each hidden layer and... | class_definition | 23,035 | 27,381 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,409 |
class MegatronBertPooler(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 t... | class_definition | 27,472 | 28,039 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,410 |
class MegatronBertPredictionHeadTransform(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:
s... | class_definition | 28,147 | 28,855 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,411 |
class MegatronBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = MegatronBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decod... | class_definition | 28,956 | 29,804 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,412 |
class MegatronBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MegatronBertLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return predict... | class_definition | 29,900 | 30,230 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,413 |
class MegatronBertOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score | class_definition | 30,326 | 30,638 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,414 |
class MegatronBertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MegatronBertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = s... | class_definition | 30,739 | 31,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,415 |
class MegatronBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MegatronBertConfig
load_tf_weights = load_tf_weights_in_megatron_bert
base_model_prefix = "bert... | class_definition | 31,221 | 32,197 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,416 |
class MegatronBertForPreTrainingOutput(ModelOutput):
"""
Output type of [`MegatronBertForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence pred... | class_definition | 32,313 | 34,279 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,417 |
class MegatronBertModel(MegatronBertPreTrainedModel):
"""
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](https://... | class_definition | 37,987 | 46,848 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,418 |
class MegatronBertForPreTraining(MegatronBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config, add_binary_head=True):
super().__init__(config)
self.bert = MegatronBertModel(config)
self.cls = MegatronBertPreTrainingHeads(config)
# In... | class_definition | 47,099 | 51,752 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,419 |
class MegatronBertForCausalLM(MegatronBertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `MegatronBertForCausalLM` as a standalone,... | class_definition | 51,905 | 58,426 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,420 |
class MegatronBertForMaskedLM(MegatronBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `MegatronBertForMaskedLM` make sure `config.is... | class_definition | 58,548 | 62,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,421 |
class MegatronBertForNextSentencePrediction(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = MegatronBertModel(config)
self.cls = MegatronBertOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
... | class_definition | 63,073 | 67,041 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,422 |
class MegatronBertForSequenceClassification(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = MegatronBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn... | class_definition | 67,280 | 71,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,423 |
class MegatronBertForMultipleChoice(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = MegatronBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize... | class_definition | 71,397 | 74,949 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,424 |
class MegatronBertForTokenClassification(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = MegatronBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 75,195 | 77,943 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,425 |
class MegatronBertForQuestionAnswering(MegatronBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = MegatronBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_lab... | class_definition | 78,247 | 82,503 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | null | 9,426 |
class MegatronBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MegatronBertModel`]. It is used to instantiate a
MEGATRON_BERT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults w... | class_definition | 814 | 6,465 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/megatron_bert/configuration_megatron_bert.py | null | 9,427 |
class GPTBigCodeAttention(nn.Module):
def __init__(self, config, is_cross_attention=False, layer_idx=None):
super().__init__()
self.config = config
self.mask_value = None
self.multi_query = config.multi_query
self.embed_dim = config.hidden_size
self.num_heads = confi... | class_definition | 2,800 | 11,750 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,428 |
class GPTBigCodeFlashAttention2(GPTBigCodeAttention):
"""
GPTBigCode flash attention module. This module inherits from `GPTBigCodeAttention` as the weights of the module
stays untouched. The only required change would be on the forward pass where it needs to correctly call the public
API of flash attent... | class_definition | 11,753 | 17,869 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,429 |
class GPTBigCodeSdpaAttention(GPTBigCodeAttention):
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
if head_mask is not None:
# The super dispatch is done in the forward.
raise ValueError(
"PyTorch SDPA does not support head_mask. Please open ... | class_definition | 17,872 | 25,211 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,430 |
class GPTBigCodeMLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = nn.Linear(embed_dim, intermediate_size)
self.c_proj = nn.Linear(intermediate_size, embed_dim)
self.act = ACT2FN[config.activation_funct... | class_definition | 25,214 | 25,990 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,431 |
class GPTBigCodeBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
... | class_definition | 26,153 | 29,855 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,432 |
class GPTBigCodePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTBigCodeConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no... | class_definition | 29,858 | 32,055 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,433 |
class GPTBigCodeModel(GPTBigCodePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.multi_query = config.multi_query
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_posi... | class_definition | 37,202 | 48,638 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,434 |
class GPTBigCodeForCausalLM(GPTBigCodePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = GPTBigCodeModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
... | class_definition | 48,853 | 56,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,435 |
class GPTBigCodeForSequenceClassification(GPTBigCodePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTBigCodeModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Init... | class_definition | 57,066 | 62,240 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,436 |
class GPTBigCodeForTokenClassification(GPTBigCodePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTBigCodeModel(config)
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
... | class_definition | 62,483 | 65,767 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py | null | 9,437 |
class GPTBigCodeConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`GPTBigCodeModel`]. It is used to instantiate a
GPTBigCode model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yiel... | class_definition | 777 | 6,277 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_bigcode/configuration_gpt_bigcode.py | null | 9,438 |
class FunnelTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" Funnel Transformer tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this super... | class_definition | 1,206 | 8,642 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel_fast.py | null | 9,439 |
class FunnelTokenizer(PreTrainedTokenizer):
r"""
Construct a Funnel Transformer 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.
Args:
... | class_definition | 1,855 | 13,895 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py | null | 9,440 |
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... | class_definition | 13,970 | 20,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py | null | 9,441 |
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... | class_definition | 20,797 | 22,685 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/tokenization_funnel.py | null | 9,442 |
class TFFunnelEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.initializer_std = 1.0 if ... | class_definition | 1,848 | 3,712 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,443 |
class TFFunnelAttentionStructure:
"""
Contains helpers for `TFFunnelRelMultiheadAttention `.
"""
cls_token_type_id: int = 2
def __init__(self, config):
self.d_model = config.d_model
self.attention_type = config.attention_type
self.num_blocks = config.num_blocks
self... | class_definition | 3,715 | 15,750 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,444 |
class TFFunnelRelMultiheadAttention(keras.layers.Layer):
def __init__(self, config, block_index, **kwargs):
super().__init__(**kwargs)
self.attention_type = config.attention_type
self.n_head = n_head = config.n_head
self.d_head = d_head = config.d_head
self.d_model = d_model ... | class_definition | 16,694 | 25,593 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,445 |
class TFFunnelPositionwiseFFN(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
initializer = get_initializer(config.initializer_range)
self.linear_1 = keras.layers.Dense(config.d_inner, kernel_initializer=initializer, name="linear_1")
self.activat... | class_definition | 25,596 | 27,308 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,446 |
class TFFunnelLayer(keras.layers.Layer):
def __init__(self, config, block_index, **kwargs):
super().__init__(**kwargs)
self.attention = TFFunnelRelMultiheadAttention(config, block_index, name="attention")
self.ffn = TFFunnelPositionwiseFFN(config, name="ffn")
def call(self, query, key, ... | class_definition | 27,311 | 28,361 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,447 |
class TFFunnelEncoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.separate_cls = config.separate_cls
self.pool_q_only = config.pool_q_only
self.block_repeats = config.block_repeats
self.attention_structure = TFFunnelAttentionStru... | class_definition | 28,364 | 31,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,448 |
class TFFunnelDecoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.separate_cls = config.separate_cls
self.truncate_seq = config.truncate_seq
self.stride = 2 ** (len(config.block_sizes) - 1)
self.attention_structure = TFFunnelAtte... | class_definition | 32,387 | 34,736 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,449 |
class TFFunnelBaseLayer(keras.layers.Layer):
"""Base model without decoder"""
config_class = FunnelConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = con... | class_definition | 34,759 | 37,426 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,450 |
class TFFunnelMainLayer(keras.layers.Layer):
"""Base model with decoder"""
config_class = FunnelConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.block_sizes = config.block_sizes
self.output_attentions = config.output_attenti... | class_definition | 37,449 | 41,507 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,451 |
class TFFunnelDiscriminatorPredictions(keras.layers.Layer):
"""Prediction module for the discriminator, made up of two dense layers."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
initializer = get_initializer(config.initializer_range)
self.dense = keras.layers.Dens... | class_definition | 41,510 | 42,852 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,452 |
class TFFunnelMaskedLMHead(keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.input_embeddings = input_embeddings
def build(self, input_shape):
self.bias ... | class_definition | 42,855 | 44,236 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,453 |
class TFFunnelClassificationHead(keras.layers.Layer):
def __init__(self, config, n_labels, **kwargs):
super().__init__(**kwargs)
initializer = get_initializer(config.initializer_range)
self.linear_hidden = keras.layers.Dense(config.d_model, kernel_initializer=initializer, name="linear_hidden... | class_definition | 44,239 | 45,483 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,454 |
class TFFunnelPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FunnelConfig
base_model_prefix = "funnel"
@property
def dummy_inputs(self):
# Funnel... | class_definition | 45,486 | 45,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,455 |
class TFFunnelForPreTrainingOutput(ModelOutput):
"""
Output type of [`FunnelForPreTraining`].
Args:
logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Prediction scores of the head (scores for each token before SoftMax).
hidden_states (`tuple(tf.Tensor)`, *optional*,... | class_definition | 45,959 | 47,207 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,456 |
class TFFunnelBaseModel(TFFunnelPreTrainedModel):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelBaseLayer(config, name="funnel")
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_... | class_definition | 52,748 | 54,765 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,457 |
class TFFunnelModel(TFFunnelPreTrainedModel):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelMainLayer(config, name="funnel")
@unpack_inputs
@add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING... | class_definition | 54,937 | 56,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,458 |
class TFFunnelForPreTraining(TFFunnelPreTrainedModel):
def __init__(self, config: FunnelConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.funnel = TFFunnelMainLayer(config, name="funnel")
self.discriminator_predictions = TFFunnelDiscriminatorPredictions(config, name="discri... | class_definition | 57,137 | 60,247 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,459 |
class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelMainLayer(config, name="funnel")
self.lm_head = TFFunnelMaskedLMHead(confi... | class_definition | 60,356 | 63,848 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,460 |
class TFFunnelForSequenceClassification(TFFunnelPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.funnel = TFFunnelBaseLayer(config, name="... | class_definition | 64,074 | 67,420 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,461 |
class TFFunnelForMultipleChoice(TFFunnelPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.funnel = TFFunnelBaseLayer(config, name="funnel")
self.classifier = TFFunnelClassificationHea... | class_definition | 67,655 | 71,946 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,462 |
class TFFunnelForTokenClassification(TFFunnelPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.funnel = TFFunnelMainLayer(config, name="funnel... | class_definition | 72,179 | 75,524 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,463 |
class TFFunnelForQuestionAnswering(TFFunnelPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.funnel = TFFunnelMainLayer(config, name="funnel")
... | class_definition | 75,815 | 80,163 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_tf_funnel.py | null | 9,464 |
class FunnelConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FunnelModel`] or a [`TFBertModel`]. It is used to
instantiate a Funnel Transformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with ... | class_definition | 761 | 7,650 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/configuration_funnel.py | null | 9,465 |
class FunnelEmbeddings(nn.Module):
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
... | class_definition | 4,967 | 5,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,466 |
class FunnelAttentionStructure(nn.Module):
"""
Contains helpers for `FunnelRelMultiheadAttention `.
"""
cls_token_type_id: int = 2
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.config = config
self.sin_dropout = nn.Dropout(config.hidden_dropout)
... | class_definition | 5,700 | 18,172 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,467 |
class FunnelRelMultiheadAttention(nn.Module):
def __init__(self, config: FunnelConfig, block_index: int) -> None:
super().__init__()
self.config = config
self.block_index = block_index
d_model, n_head, d_head = config.d_model, config.n_head, config.d_head
self.hidden_dropout... | class_definition | 19,145 | 26,083 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,468 |
class FunnelPositionwiseFFN(nn.Module):
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.linear_1 = nn.Linear(config.d_model, config.d_inner)
self.activation_function = ACT2FN[config.hidden_act]
self.activation_dropout = nn.Dropout(config.activation_dropout)
... | class_definition | 26,086 | 26,881 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,469 |
class FunnelLayer(nn.Module):
def __init__(self, config: FunnelConfig, block_index: int) -> None:
super().__init__()
self.attention = FunnelRelMultiheadAttention(config, block_index)
self.ffn = FunnelPositionwiseFFN(config)
def forward(
self,
query: torch.Tensor,
... | class_definition | 26,884 | 27,543 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,470 |
class FunnelEncoder(nn.Module):
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.config = config
self.attention_structure = FunnelAttentionStructure(config)
self.blocks = nn.ModuleList(
[
nn.ModuleList([FunnelLayer(config, block_in... | class_definition | 27,546 | 30,512 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,471 |
class FunnelDecoder(nn.Module):
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.config = config
self.attention_structure = FunnelAttentionStructure(config)
self.layers = nn.ModuleList([FunnelLayer(config, 0) for _ in range(config.num_decoder_layers)])
d... | class_definition | 31,238 | 33,229 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,472 |
class FunnelDiscriminatorPredictions(nn.Module):
"""Prediction module for the discriminator, made up of two dense layers."""
def __init__(self, config: FunnelConfig) -> None:
super().__init__()
self.config = config
self.dense = nn.Linear(config.d_model, config.d_model)
self.dens... | class_definition | 33,232 | 33,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,473 |
class FunnelPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FunnelConfig
load_tf_weights = load_tf_weights_in_funnel
base_model_prefix = "funnel"
def _init_... | class_definition | 33,903 | 35,634 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,474 |
class FunnelClassificationHead(nn.Module):
def __init__(self, config: FunnelConfig, n_labels: int) -> None:
super().__init__()
self.linear_hidden = nn.Linear(config.d_model, config.d_model)
self.dropout = nn.Dropout(config.hidden_dropout)
self.linear_out = nn.Linear(config.d_model, n... | class_definition | 35,637 | 36,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,475 |
class FunnelForPreTrainingOutput(ModelOutput):
"""
Output type of [`FunnelForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss of the ELECTRA-style objective.
logits (`torch.FloatTensor` of shape `(batch_s... | class_definition | 36,198 | 37,714 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,476 |
class FunnelBaseModel(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.embeddings = FunnelEmbeddings(config)
self.encoder = FunnelEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def... | class_definition | 41,073 | 44,067 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,477 |
class FunnelModel(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.config = config
self.embeddings = FunnelEmbeddings(config)
self.encoder = FunnelEncoder(config)
self.decoder = FunnelDecoder(config)
# Initialize... | class_definition | 44,239 | 48,299 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,478 |
class FunnelForPreTraining(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.funnel = FunnelModel(config)
self.discriminator_predictions = FunnelDiscriminatorPredictions(config)
# Initialize weights and apply final processing
... | class_definition | 48,508 | 51,967 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,479 |
class FunnelForMaskedLM(FunnelPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.funnel = FunnelModel(config)
self.lm_head = nn.Linear(config.d_model, config.vocab_size)
# Initialize weights... | class_definition | 52,088 | 54,996 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,480 |
class FunnelForSequenceClassification(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.funnel = FunnelBaseModel(config)
self.classifier = FunnelClassificationHead(... | class_definition | 55,250 | 58,899 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,481 |
class FunnelForMultipleChoice(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.funnel = FunnelBaseModel(config)
self.classifier = FunnelClassificationHead(config, 1)
# Initialize weights and apply final processing
self.p... | class_definition | 59,175 | 62,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,482 |
class FunnelForTokenClassification(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.funnel = FunnelModel(config)
self.dropout = nn.Dropout(config.hidden_dropout)
self.classifier = nn.L... | class_definition | 62,602 | 65,122 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,483 |
class FunnelForQuestionAnswering(FunnelPreTrainedModel):
def __init__(self, config: FunnelConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.funnel = FunnelModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Ini... | class_definition | 65,424 | 69,449 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/funnel/modeling_funnel.py | null | 9,484 |
class PhiAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_... | class_definition | 5,113 | 9,720 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,485 |
class PhiMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
... | class_definition | 9,723 | 10,292 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,486 |
class PhiDecoderLayer(nn.Module):
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__()
self.self_attn = PhiAttention(config, layer_idx=layer_idx)
self.mlp = PhiMLP(config)
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | class_definition | 10,295 | 12,189 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,487 |
class PhiRotaryEmbedding(nn.Module):
def __init__(self, config: PhiConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config... | class_definition | 12,192 | 15,383 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,488 |
class PhiPreTrainedModel(PreTrainedModel):
config_class = PhiConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PhiDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_f... | class_definition | 16,397 | 17,314 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,489 |
class PhiModel(PhiPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
Args:
config: PhiConfig
"""
def __init__(self, config: PhiConfig):
super().__init__(config)
self.padding_idx = config.pad_token_i... | class_definition | 22,112 | 33,500 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,490 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 33,503 | 33,565 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,491 |
class PhiForCausalLM(PhiPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = PhiModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.L... | class_definition | 33,568 | 38,674 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,492 |
class PhiForSequenceClassification(PhiPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = PhiModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and app... | class_definition | 39,461 | 43,265 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,493 |
class PhiForTokenClassification(PhiPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = PhiModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classifier_d... | class_definition | 43,508 | 46,712 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modeling_phi.py | null | 9,494 |
class PhiConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configu... | class_definition | 853 | 10,536 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/configuration_phi.py | null | 9,495 |
class PhiAttention(LlamaAttention):
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_h... | class_definition | 756 | 4,946 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py | null | 9,496 |
class PhiMLP(CLIPMLP):
pass | class_definition | 4,949 | 4,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py | null | 9,497 |
class PhiDecoderLayer(nn.Module):
def __init__(self, config: PhiConfig, layer_idx: int):
super().__init__()
self.self_attn = PhiAttention(config, layer_idx=layer_idx)
self.mlp = PhiMLP(config)
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | class_definition | 4,983 | 6,877 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py | null | 9,498 |
class PhiModel(LlamaModel):
def __init__(self, config: PhiConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.embed_dropout = nn.Dropout(config.embd_pdrop)
self... | class_definition | 6,880 | 11,776 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py | null | 9,499 |
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