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 MistralDecoderLayer(nn.Module):
def __init__(self, config: MistralConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = MistralAttention(config=config, layer_idx=layer_idx)
self.mlp = MistralMLP(config)
self.input_layernorm =... | class_definition | 10,113 | 12,193 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py | null | 9,700 |
class MistralRotaryEmbedding(nn.Module):
def __init__(self, config: MistralConfig, 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"... | class_definition | 12,196 | 15,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py | null | 9,701 |
class MistralPreTrainedModel(PreTrainedModel):
config_class = MistralConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["MistralDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
... | class_definition | 16,425 | 17,354 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py | null | 9,702 |
class MistralModel(MistralPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
Args:
config: MistralConfig
"""
def __init__(self, config: MistralConfig):
super().__init__(config)
self.padding_idx ... | class_definition | 22,164 | 35,346 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py | null | 9,703 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 35,349 | 35,411 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py | null | 9,704 |
class MistralForCausalLM(MistralPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = MistralModel(config)
self.vocab_size = config.vocab_size
self.lm... | class_definition | 35,414 | 40,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py | null | 9,705 |
class MistralForTokenClassification(MistralPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = MistralModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.... | class_definition | 40,812 | 44,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py | null | 9,706 |
class MistralForSequenceClassification(MistralPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = MistralModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize wei... | class_definition | 44,831 | 48,651 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py | null | 9,707 |
class MistralForQuestionAnswering(MistralPreTrainedModel):
base_model_prefix = "model"
def __init__(self, config):
super().__init__(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.model = MistralModel(config) # diff with Llama: transformer->model
# Initialize w... | class_definition | 48,951 | 52,360 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py | null | 9,708 |
class Qwen2MoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2MoeModel`]. It is used to instantiate a
Qwen2MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a s... | class_definition | 876 | 12,736 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py | null | 9,709 |
class Qwen2MoeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Qwen2MoeRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 5,846 | 6,572 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,710 |
class Qwen2MoeRotaryEmbedding(nn.Module):
def __init__(self, config: Qwen2MoeConfig, 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_typ... | class_definition | 6,672 | 9,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,711 |
class Qwen2MoeMLP(nn.Module):
def __init__(self, config, intermediate_size=None):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias... | class_definition | 11,839 | 12,497 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,712 |
class Qwen2MoeAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
and "Generating Long Sequences with Sparse Transformers".
"""
def __init__(self, config: Qwen2MoeConfig, layer_idx: Optional[int] = None):
... | class_definition | 13,310 | 18,162 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,713 |
class Qwen2MoeFlashAttention2(Qwen2MoeAttention):
"""
Qwen2Moe flash attention module, following Qwen2Moe attention module. This module inherits from `Qwen2MoeAttention`
as the weights of the module stays untouched. The only required change would be on the forward pass
where it needs to correctly call t... | class_definition | 18,300 | 23,531 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,714 |
class Qwen2MoeSdpaAttention(Qwen2MoeAttention):
"""
Qwen2Moe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`Qwen2MoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# ... | class_definition | 23,667 | 28,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,715 |
class Qwen2MoeSparseMoeBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_experts = config.num_experts
self.top_k = config.num_experts_per_tok
self.norm_topk_prob = config.norm_topk_prob
# gating
self.gate = nn.Linear(config.hidden_size, config... | class_definition | 28,478 | 31,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,716 |
class Qwen2MoeDecoderLayer(nn.Module):
def __init__(self, config: Qwen2MoeConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = QWEN2MOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
if (layer_idx not in config.mlp_on... | class_definition | 31,652 | 36,099 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,717 |
class Qwen2MoePreTrainedModel(PreTrainedModel):
config_class = Qwen2MoeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Qwen2MoeDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = True
... | class_definition | 37,133 | 37,961 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,718 |
class Qwen2MoeModel(Qwen2MoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2MoeDecoderLayer`]
Args:
config: Qwen2MoeConfig
"""
def __init__(self, config: Qwen2MoeConfig):
super().__init__(config)
self.padding... | class_definition | 43,005 | 58,087 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,719 |
class Qwen2MoeForCausalLM(Qwen2MoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = Qwen2MoeModel(config)
self.vocab_size = config.vocab_size
self... | class_definition | 58,090 | 64,207 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,720 |
class Qwen2MoeForSequenceClassification(Qwen2MoePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Qwen2MoeModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize ... | class_definition | 65,133 | 68,957 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,721 |
class Qwen2MoeForTokenClassification(Qwen2MoePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Qwen2MoeModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = conf... | class_definition | 69,331 | 72,555 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,722 |
class Qwen2MoeForQuestionAnswering(Qwen2MoePreTrainedModel):
base_model_prefix = "model"
def __init__(self, config):
super().__init__(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.model = Qwen2MoeModel(config) # diff with Llama: transformer->model
# Initializ... | class_definition | 72,986 | 76,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py | null | 9,723 |
class NemotronConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NemotronModel`]. It is used to instantiate an Nemotron
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a ... | class_definition | 902 | 7,361 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py | null | 9,724 |
class NemotronLayerNorm1P(nn.LayerNorm):
def __init__(
self,
normalized_shape: Union[int, List[int], Size],
eps: float = 1e-5,
elementwise_affine: bool = True,
bias: bool = True,
device=None,
dtype=None,
):
super().__init__(normalized_shape, eps, e... | class_definition | 2,095 | 2,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,725 |
class NemotronRotaryEmbedding(nn.Module):
# Ignore copy
def __init__(
self,
config: NemotronConfig,
device=None,
):
super().__init__()
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = conf... | class_definition | 2,901 | 5,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,726 |
class NemotronMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
... | class_definition | 7,975 | 8,527 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,727 |
class NemotronAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: NemotronConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | class_definition | 9,204 | 13,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,728 |
class NemotronFlashAttention2(NemotronAttention):
"""
Nemotron flash attention module. This module inherits from `NemotronAttention` 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 attention and ... | class_definition | 13,641 | 19,353 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,729 |
class NemotronSdpaAttention(NemotronAttention):
"""
Nemotron attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`NemotronAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# ... | class_definition | 19,521 | 24,087 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,730 |
class NemotronDecoderLayer(nn.Module):
# Ignore copy
def __init__(self, config: NemotronConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = NEMOTRON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
... | class_definition | 24,413 | 28,211 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,731 |
class NemotronPreTrainedModel(PreTrainedModel):
config_class = NemotronConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["NemotronDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
... | class_definition | 29,245 | 30,146 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,732 |
class NemotronModel(NemotronPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`NemotronDecoderLayer`]
Args:
config: NemotronConfig
"""
def __init__(self, config: NemotronConfig):
super().__init__(config)
self.padding... | class_definition | 34,959 | 46,760 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,733 |
class NemotronForCausalLM(NemotronPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = NemotronModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, c... | class_definition | 46,911 | 51,998 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,734 |
class NemotronForSequenceClassification(NemotronPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = NemotronModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize ... | class_definition | 52,939 | 56,763 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,735 |
class NemotronForQuestionAnswering(NemotronPreTrainedModel):
base_model_prefix = "transformer"
# Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Nemotron
def __init__(self, config):
super().__init__(config)
self.transformer = NemotronModel... | class_definition | 57,199 | 60,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,736 |
class NemotronForTokenClassification(NemotronPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = NemotronModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = conf... | class_definition | 61,107 | 64,331 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py | null | 9,737 |
class FNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FNetModel`]. It is used to instantiate an FNet
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | class_definition | 795 | 5,539 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/configuration_fnet.py | null | 9,738 |
class FNetTokenizer(PreTrainedTokenizer):
"""
Construct an FNet tokenizer. Adapted from [`AlbertTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`]
which contains most of the main methods. Users should refer to this supercl... | class_definition | 1,048 | 14,547 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet.py | null | 9,739 |
class FNetEmbeddings(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_embeddi... | class_definition | 2,888 | 5,901 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,740 |
class FNetBasicFourierTransform(nn.Module):
def __init__(self, config):
super().__init__()
self._init_fourier_transform(config)
def _init_fourier_transform(self, config):
if not config.use_tpu_fourier_optimizations:
self.fourier_transform = partial(torch.fft.fftn, dim=(1, 2)... | class_definition | 5,904 | 7,627 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,741 |
class FNetBasicOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states, input_tensor):
hidden_states = self.LayerNorm(input_tensor + hidden_states)
return hidde... | class_definition | 7,630 | 7,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,742 |
class FNetFourierTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.self = FNetBasicFourierTransform(config)
self.output = FNetBasicOutput(config)
def forward(self, hidden_states):
self_outputs = self.self(hidden_states)
fourier_output = self.outpu... | class_definition | 7,961 | 8,373 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,743 |
class FNetIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.interm... | class_definition | 8,462 | 9,027 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,744 |
class FNetOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def... | class_definition | 9,110 | 9,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,745 |
class FNetLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1 # The dimension which has the sequence length
self.fourier = FNetFourierTransform(config)
self.intermediate = FNetInt... | class_definition | 9,721 | 10,681 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,746 |
class FNetEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([FNetLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(self, hidden_states, output_hidden_states=Fa... | class_definition | 10,684 | 11,867 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,747 |
class FNetPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidde... | class_definition | 11,950 | 12,509 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,748 |
class FNetPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.tran... | class_definition | 12,609 | 13,309 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,749 |
class FNetLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = FNetPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(c... | class_definition | 13,312 | 14,314 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,750 |
class FNetOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = FNetLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 14,317 | 14,601 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,751 |
class FNetOnlyNSPHead(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 | 14,689 | 14,993 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,752 |
class FNetPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = FNetLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(... | class_definition | 15,086 | 15,549 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,753 |
class FNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FNetConfig
base_model_prefix = "fnet"
supports_gradient_checkpointing = True
def _init_weights(sel... | class_definition | 15,552 | 16,746 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,754 |
class FNetForPreTrainingOutput(ModelOutput):
"""
Output type of [`FNetForPreTraining`].
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 prediction
... | class_definition | 16,760 | 18,217 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,755 |
class FNetModel(FNetPreTrainedModel):
"""
The model can behave as an encoder, following the architecture described in [FNet: Mixing Tokens with Fourier
Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
"""
def __init__(self, config,... | class_definition | 20,762 | 24,798 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,756 |
class FNetForPreTraining(FNetPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.fnet = FNetModel(config)
self.cls = FNetPreTrainingHeads(config)
# Initialize weight... | class_definition | 25,032 | 29,231 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,757 |
class FNetForMaskedLM(FNetPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.fnet = FNetModel(config)
self.cls = FNetOnlyMLMHead(config)
# Initialize weights and ap... | class_definition | 29,336 | 32,061 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,758 |
class FNetForNextSentencePrediction(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.fnet = FNetModel(config)
self.cls = FNetOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_mod... | class_definition | 32,201 | 35,726 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,759 |
class FNetForSequenceClassification(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.fnet = FNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_si... | class_definition | 35,948 | 39,382 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,760 |
class FNetForMultipleChoice(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.fnet = FNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final... | class_definition | 39,613 | 42,603 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,761 |
class FNetForTokenClassification(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.fnet = FNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size... | class_definition | 42,832 | 45,171 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,762 |
class FNetForQuestionAnswering(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.fnet = FNetModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply fin... | class_definition | 45,458 | 49,262 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py | null | 9,763 |
class FNetTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" FNetTokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`AlbertTokenizerFast`]. Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokeniz... | class_definition | 1,192 | 8,061 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet_fast.py | null | 9,764 |
class CanineConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CanineModel`]. It is used to instantiate an
CANINE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | class_definition | 792 | 6,554 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/configuration_canine.py | null | 9,765 |
class CanineModelOutputWithPooling(ModelOutput):
"""
Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly
different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow
Transformer encoders.
A... | class_definition | 1,758 | 4,332 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,766 |
class CanineEmbeddings(nn.Module):
"""Construct the character, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.config = config
# character embeddings
shard_embedding_size = config.hidden_size // config.num_hash_functions
for i... | class_definition | 8,310 | 12,630 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,767 |
class CharactersToMolecules(nn.Module):
"""Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions."""
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
in_channels=config.hidden_size,
out_channels=config.... | class_definition | 12,633 | 14,596 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,768 |
class ConvProjection(nn.Module):
"""
Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size
characters.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.conv = nn.Conv1d(
in_... | class_definition | 14,599 | 17,075 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,769 |
class CanineSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the numb... | class_definition | 17,078 | 22,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,770 |
class CanineSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def... | class_definition | 22,627 | 23,297 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,771 |
class CanineAttention(nn.Module):
"""
Additional arguments related to local attention:
- **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention.
- **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to
... | class_definition | 23,300 | 30,681 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,772 |
class CanineIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.inte... | class_definition | 30,684 | 31,261 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,773 |
class CanineOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
d... | class_definition | 31,264 | 31,896 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,774 |
class CanineLayer(nn.Module):
def __init__(
self,
config,
local,
always_attend_to_first_position,
first_position_attends_to_all,
attend_from_chunk_width,
attend_from_chunk_stride,
attend_to_chunk_width,
attend_to_chunk_stride,
):
su... | class_definition | 31,899 | 33,859 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,775 |
class CanineEncoder(nn.Module):
def __init__(
self,
config,
local=False,
always_attend_to_first_position=False,
first_position_attends_to_all=False,
attend_from_chunk_width=128,
attend_from_chunk_stride=128,
attend_to_chunk_width=128,
attend_to... | class_definition | 33,862 | 36,626 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,776 |
class CaninePooler(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: Tuple[torch.FloatTensor]) -> torch.FloatTensor:
# We "pool" the model by simp... | class_definition | 36,629 | 37,207 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,777 |
class CaninePredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.tr... | class_definition | 37,210 | 37,929 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,778 |
class CanineLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = CaninePredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Line... | class_definition | 37,932 | 38,748 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,779 |
class CanineOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = CanineLMPredictionHead(config)
def forward(
self,
sequence_output: Tuple[torch.Tensor],
) -> Tuple[torch.Tensor]:
prediction_scores = self.predictions(sequence_outpu... | class_definition | 38,751 | 39,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,780 |
class CaninePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CanineConfig
load_tf_weights = load_tf_weights_in_canine
base_model_prefix = "canine"
supports_gr... | class_definition | 39,109 | 40,280 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,781 |
class CanineModel(CaninePreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
shallow_config = copy.deepcopy(config)
shallow_config.num_hidden_layers = 1
self.char_embeddings = CanineEmbeddings(config)
# s... | class_definition | 43,708 | 56,967 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,782 |
class CanineForSequenceClassification(CaninePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.canine = CanineModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.h... | class_definition | 57,193 | 61,041 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,783 |
class CanineForMultipleChoice(CaninePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.canine = CanineModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and app... | class_definition | 61,276 | 64,793 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,784 |
class CanineForTokenClassification(CaninePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.canine = CanineModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidd... | class_definition | 65,026 | 68,871 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,785 |
class CanineForQuestionAnswering(CaninePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.canine = CanineModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and ap... | class_definition | 69,162 | 73,393 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py | null | 9,786 |
class CanineTokenizer(PreTrainedTokenizer):
r"""
Construct a CANINE tokenizer (i.e. a character splitter). It turns text into a sequence of characters, and then
converts each character into its Unicode code point.
[`CanineTokenizer`] inherits from [`PreTrainedTokenizer`].
Refer to superclass [`Pre... | class_definition | 1,968 | 9,286 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py | null | 9,787 |
class TFAttention(keras.layers.Layer):
def __init__(self, nx, config, scale=False, **kwargs):
super().__init__(**kwargs)
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implementation]
assert (
... | class_definition | 1,766 | 5,870 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,788 |
class TFMLP(keras.layers.Layer):
def __init__(self, n_state, config, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc")
self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initia... | class_definition | 5,873 | 6,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,789 |
class TFBlock(keras.layers.Layer):
def __init__(self, config, scale=False, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
self.attn = TFAttention(nx, config, scale, name="attn")
self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
... | class_definition | 6,983 | 8,593 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,790 |
class TFOpenAIGPTMainLayer(keras.layers.Layer):
config_class = OpenAIGPTConfig
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_... | class_definition | 8,616 | 16,064 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,791 |
class TFOpenAIGPTPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = OpenAIGPTConfig
base_model_prefix = "transformer" | class_definition | 16,067 | 16,338 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,792 |
class TFOpenAIGPTDoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
logits (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling ... | class_definition | 16,352 | 17,926 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,793 |
class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRIN... | class_definition | 23,961 | 25,791 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,794 |
class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
# OpenAIGPT does not have past caching featur... | class_definition | 26,000 | 29,275 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,795 |
class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
config.num_labels = 1
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
self.multiple_choice_head = TFSequenceSu... | class_definition | 29,728 | 35,520 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,796 |
class TFOpenAIGPTForSequenceClassification(TFOpenAIGPTPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.score = keras.layers.Dense(
config.num_labels,... | class_definition | 36,329 | 41,055 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py | null | 9,797 |
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 | 1,386 | 8,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py | null | 9,798 |
class OpenAIGPTTokenizer(PreTrainedTokenizer):
"""
Construct a GPT Tokenizer. Based on Byte-Pair-Encoding with the following peculiarities:
- lowercases all inputs,
- uses `SpaCy` tokenizer and `ftfy` for pre-BPE tokenization if they are installed, fallback to BERT's
`BasicTokenizer` if not.
... | class_definition | 9,001 | 15,151 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py | null | 9,799 |
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